<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">AM</journal-id><journal-title-group><journal-title>Applied Mathematics</journal-title></journal-title-group><issn pub-type="epub">2152-7385</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/am.2015.68132</article-id><article-id pub-id-type="publisher-id">AM-58453</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Physics&amp;Mathematics</subject></subj-group></article-categories><title-group><article-title>
 
 
  Universality in Statistical Measures of Trajectories in Classical Billiard Systems
 
</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>ean-Fran&amp;ccedil;ois</surname><given-names>Laprise</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Ahmad</surname><given-names>Hosseinizadeh</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Helmut</surname><given-names>Kr&amp;ouml;ger</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib></contrib-group><aff id="aff1"><addr-line>Department of Physics, Laval University, Qu&amp;amp;eacute;bec, Canada</addr-line></aff><author-notes><corresp id="cor1">* E-mail:<email>jean-francois.laprise@crchudequebec.ulaval.ca(EL)</email>;<email>hosseina@uwm.edu(AH)</email>;<email>helmut.kroger@phy.ulaval.ca(HK)</email>;</corresp></author-notes><pub-date pub-type="epub"><day>03</day><month>07</month><year>2015</year></pub-date><volume>06</volume><issue>08</issue><fpage>1407</fpage><lpage>1425</lpage><history><date date-type="received"><day>13</day>	<month>January</month>	<year>2015</year></date><date date-type="rev-recd"><day>accepted</day>	<month>27</month>	<year>July</year>	</date><date date-type="accepted"><day>30</day>	<month>July</month>	<year>2015</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
 
 
  For classical billiards, we suggest that a matrix of action or length of trajectories in conjunction with statistical measures, level spacing distribution and spectral rigidity, can be used to distinguish chaotic from integrable systems. As examples of 2D chaotic billiards, we considered the Bunimovich stadium billiard and the Sinai billiard. In the level spacing distribution and spectral rigidity, we found GOE behaviour consistent with predictions from random matrix theory. We studied transport properties and computed a diffusion coefficient. For the Sinai billiard, we found normal diffusion, while the stadium billiard showed anomalous diffusion behaviour. As example of a 2D integrable billiard, we considered the rectangular billiard. We found very rigid behaviour with strongly correlated spectra similar to a Dirac comb. These findings present numerical evidence for universality in level spacing fluctuations to hold in classically integrable systems and in classically fully chaotic systems.
 
</p></abstract><kwd-group><kwd>Classical Chaos</kwd><kwd> Dynamical Billiards</kwd><kwd> Random Matrix Theory</kwd><kwd> Level Spacing Fluctuations</kwd><kwd> Universality</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. Introduction</title><p>The idea to model apparently disordered spectra, like those of heavy nuclei, using random matrices was sug- gested in the mid-50’s by Wigner, and then formalized in the early 60’s in the work of Dyson and Mehta [<xref ref-type="bibr" rid="scirp.58453-ref1">1</xref>] -[<xref ref-type="bibr" rid="scirp.58453-ref6">6</xref>] . They showed that random matrices of Gaussian orthogonal ensembles (GOE) generate a Wigner-type nearest- neighbour level spacing (NNS) distribution [<xref ref-type="bibr" rid="scirp.58453-ref6">6</xref>] . In a seminal paper, Bohigas, Giannoni and Schmit (BGS) for- mulated a conjecture [<xref ref-type="bibr" rid="scirp.58453-ref7">7</xref>] stating that time-reversal invariant quantum systems with classically fully chaotic (ergodic) counterpart have universality properties given by random matrix theory (RMT). Experiments in nu- clear physics, for example, have shown that spectra originating from different heavy nuclei give the same Wignerian energy level spacing distribution [<xref ref-type="bibr" rid="scirp.58453-ref8">8</xref>] . Universality properties in quantum chaos of bound systems, i.e. quantum systems with a fully chaotic classical counterpart, have now been demonstrated in many experiments, computational models and in theoretical studies [<xref ref-type="bibr" rid="scirp.58453-ref6">6</xref>] [<xref ref-type="bibr" rid="scirp.58453-ref9">9</xref>] -[<xref ref-type="bibr" rid="scirp.58453-ref14">14</xref>] . Theoretical support of the BGS conjecture came from the semiclassical theory of spectral rigidity by Berry [<xref ref-type="bibr" rid="scirp.58453-ref15">15</xref>] [<xref ref-type="bibr" rid="scirp.58453-ref16">16</xref>] , who showed that universal behaviour in the energy level statistics is due to long classical orbits. Sieber and Richter [<xref ref-type="bibr" rid="scirp.58453-ref17">17</xref>] investigated the role of correlations between classical orbits. The semiclassical theory has been further developed by M&#252;ller et al. [<xref ref-type="bibr" rid="scirp.58453-ref18">18</xref>] [<xref ref-type="bibr" rid="scirp.58453-ref19">19</xref>] . In ref. [<xref ref-type="bibr" rid="scirp.58453-ref19">19</xref>] they presented the “core of a proof” of the BGS conjecture, which, by proving arguments previously used by Berry [<xref ref-type="bibr" rid="scirp.58453-ref15">15</xref>] , show that in the semi-classical limit the periodic classical orbits determine the universal fluctua- tions of quantum energy levels. Further refinements have been made by Keating and M&#252;ller [<xref ref-type="bibr" rid="scirp.58453-ref20">20</xref>] .</p><p>The study of classical strongly chaotic systems (Anosov systems) has revealed that central limit theorems (CLT) hold [<xref ref-type="bibr" rid="scirp.58453-ref21">21</xref>] -[<xref ref-type="bibr" rid="scirp.58453-ref26">26</xref>] . This has been proven for the 2D periodic Lorentz gas with finite horizon. The first step of a proof was given by Bunimovich and Sinai [<xref ref-type="bibr" rid="scirp.58453-ref21">21</xref>] and was completed by Bunimovich, Sinai and Chernov [<xref ref-type="bibr" rid="scirp.58453-ref23">23</xref>] . At macroscopic times, such deterministic chaotic system converges to Brownian motion, i.e. behaves like a random system [<xref ref-type="bibr" rid="scirp.58453-ref21">21</xref>] [<xref ref-type="bibr" rid="scirp.58453-ref23">23</xref>] [<xref ref-type="bibr" rid="scirp.58453-ref24">24</xref>] [<xref ref-type="bibr" rid="scirp.58453-ref26">26</xref>] . This is also supported by the existence of an average diffusion coefficient [<xref ref-type="bibr" rid="scirp.58453-ref21">21</xref>] [<xref ref-type="bibr" rid="scirp.58453-ref23">23</xref>] [<xref ref-type="bibr" rid="scirp.58453-ref27">27</xref>] , showing the diffusive character of such chaotic system.</p><p>Do classical fully chaotic systems also exhibit universality properties? This question was addressed by Argaman et al. [<xref ref-type="bibr" rid="scirp.58453-ref28">28</xref>] , who showed that there is universal behaviour in 2 points correlation functions of actions of periodic orbits of classically chaotic systems. As examples they considered the deformed cat-map and the baker-map. This has been elaborated further in a number of studies by Dittes et al. [<xref ref-type="bibr" rid="scirp.58453-ref29">29</xref>] , Aurich and Sieber [<xref ref-type="bibr" rid="scirp.58453-ref30">30</xref>] , Cohen et al. [<xref ref-type="bibr" rid="scirp.58453-ref31">31</xref>] , Tanner [<xref ref-type="bibr" rid="scirp.58453-ref32">32</xref>] , Sano [<xref ref-type="bibr" rid="scirp.58453-ref33">33</xref>] , Primack and Smilansky [<xref ref-type="bibr" rid="scirp.58453-ref34">34</xref>] , Sieber and Richter [<xref ref-type="bibr" rid="scirp.58453-ref17">17</xref>] and Smi- lansky and Verdene [<xref ref-type="bibr" rid="scirp.58453-ref35">35</xref>] . Argaman et al. started from the assumption that spectral fluctuations of chaotic quantum systems follow the predictions of RMT and they derived a universal expression for classical cor- relation functions of periodic orbits via Gutzwiller’s semi-classical trace formula. They concluded “The real challenge, though, is to find out whether these action correlations can be explained on a completely classical level”.</p><p>An answer was proposed by Laprise et al. [<xref ref-type="bibr" rid="scirp.58453-ref36">36</xref>] [<xref ref-type="bibr" rid="scirp.58453-ref37">37</xref>] who found universal behaviour in classical 2D billiards by looking at fluctuations in spectra of classical action/length matrices from billiard trajectories. They showed that one could distinguish chaos from integrability in classical systems using RMT and an analogue of the BGS- conjecture. In particular, they considered the Lima&#231;on/Robnik family of billiards, which interpolates between the chaotic cardiod billiard and the integrable circular billiard. For the cardioid billiard, the level spacing dis- tribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x5.png" xlink:type="simple"/></inline-formula> and spectral rigidity <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x6.png" xlink:type="simple"/></inline-formula> were found to be consistent with the GOE behaviour predicted by RMT. For the interpolating case close to the circle, the behaviour approached a Poissonian distribution. The circular billiard itself was found to be very rigid and strongly correlated and yielded<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x7.png" xlink:type="simple"/></inline-formula>. The jump in behaviour at the transition to the circle is associated with the corresponding change in the symmetry group.</p><p>This article extends the results of reference [<xref ref-type="bibr" rid="scirp.58453-ref36">36</xref>] in the following directions: 1) We consider the 2D rec- tangular billiard as another example of an integrable billiard. Compared to the circular billiard, this billiard has lower symmetry (no group property). Nevertheless, it displays strong spectral correlation and rigidity like the circular billiard. 2) We present numerical studies for other chaotic 2D billiards: the Sinai-billiard and the Bunimovich stadium billiard. 3) In order to understand the observed universal behaviour in chaotic billiards, we present arguments linking such behaviour to CLTs, diffusive and random walk behaviour. In particular, we present a mathematically rigorous result on the distribution of length of trajectories.</p><p>The answers we found can be summarized as follows: For the 2D Sinai billiard and the 2D Bunimovich stadium billiard the level spacing distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x8.png" xlink:type="simple"/></inline-formula> and spectral rigidity <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x9.png" xlink:type="simple"/></inline-formula> are consistent with pre- dictions of RMT (GOE behaviour), i.e. show universal behaviour. This behaviour is statistically the same as the one observed in quantum chaos (obtained from energy level spacing distributions). The implication of these findings is that RMT not only represents well the statistical fluctuation properties of the energy spectrum of chaotic quantum systems, but also those of the length spectrum of chaotic classical systems. Moreover, sta- tistical fluctuations obtained from spectra of action/length matrices clearly distinguish chaotic from integrable systems.</p></sec><sec id="s2"><title>2. Length and Action of Trajectories</title><p>In classical systems, chaos information is encoded in trajectories. According to the Alekseev-Brudno theorem [<xref ref-type="bibr" rid="scirp.58453-ref38">38</xref>] the temporal length t is related via the Kolmogorov-Sinai K entropy to the information <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x10.png" xlink:type="simple"/></inline-formula> in the segment of trajectory,</p><disp-formula id="scirp.58453-formula962"><label>(1)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x11.png"  xlink:type="simple"/></disp-formula><p>This motivates us to look at the length of trajectories and its fluctuation properties. Let <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x12.png" xlink:type="simple"/></inline-formula> denote the Lagrangian of a system, let <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x13.png" xlink:type="simple"/></inline-formula> denote a solution (trajectory) of the Euler-Lagrange equations, with boundary points <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x14.png" xlink:type="simple"/></inline-formula> and<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x15.png" xlink:type="simple"/></inline-formula>. Let</p><disp-formula id="scirp.58453-formula963"><label>(2)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x16.png"  xlink:type="simple"/></disp-formula><p>denote the action over <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x17.png" xlink:type="simple"/></inline-formula> and let</p><disp-formula id="scirp.58453-formula964"><label>(3)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x18.png"  xlink:type="simple"/></disp-formula><p>denote the length of the trajectory<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x19.png" xlink:type="simple"/></inline-formula>. We choose a finite set of discrete points<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x20.png" xlink:type="simple"/></inline-formula>. For all pairs of boundary points<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x20.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x21.png" xlink:type="simple"/></inline-formula>, we compute a classical trajectory<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x20.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x21.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x22.png" xlink:type="simple"/></inline-formula>, connecting those points. We suggest the construction of an action matrix and a length matrix,</p><disp-formula id="scirp.58453-formula965"><graphic  xlink:href="http://html.scirp.org/file/25-7402611x23.png"  xlink:type="simple"/></disp-formula><disp-formula id="scirp.58453-formula966"><label>(4)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x24.png"  xlink:type="simple"/></disp-formula><p>where i and j are respectively the indices of the final and initial boundary points of the trajectory. Both matrices are viable for statistical analysis of classical chaos. In the case of billiard systems, we consider trajectories where the billiard particle moves with constant velocity u and constant kinetic energy E. Then the action and the length matrix are essentially equivalent,</p><disp-formula id="scirp.58453-formula967"><label>(5)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x25.png"  xlink:type="simple"/></disp-formula>Numerical Calculation of Length Matrices<p>When solving for two boundary points <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x26.png" xlink:type="simple"/></inline-formula> and <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x26.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x27.png" xlink:type="simple"/></inline-formula> in a billiard the solution is generally not unique; the number of possible trajectories varies with the number <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x26.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x27.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x28.png" xlink:type="simple"/></inline-formula> of rebounds. We have thus classified trajectories-and corresponding length matrices-according to<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x26.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x27.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x28.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x29.png" xlink:type="simple"/></inline-formula>.</p><p>For <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x30.png" xlink:type="simple"/></inline-formula> the number of trajectories is quite large<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x30.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x31.png" xlink:type="simple"/></inline-formula>. In order to limit computational cost, we considered smaller subsets of length matrices, in the following way. For a given number of rebounds <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x30.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x31.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x32.png" xlink:type="simple"/></inline-formula> and a given pair of boundary points<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x30.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x31.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x32.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x33.png" xlink:type="simple"/></inline-formula>, we determined the starting angles <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x30.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x31.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x32.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x33.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x34.png" xlink:type="simple"/></inline-formula> (measured from the normal to the billiard wall at the boundary point) which corresponds to a trajectory<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x30.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x31.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x32.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x33.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x34.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x35.png" xlink:type="simple"/></inline-formula>. That search was carried out in the</p><p>range<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x36.png" xlink:type="simple"/></inline-formula>. The subsets have been constructed by introducing an upper bound <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x36.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x37.png" xlink:type="simple"/></inline-formula> on</p><p>the number of trajectories and retaining only the trajectories corresponding to the starting angles<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x38.png" xlink:type="simple"/></inline-formula>. We repeated this for all combinations of boundary points. We then checked that this procedure did not spoil the statistical properties we aimed to measure by considering different cutoff <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x38.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x39.png" xlink:type="simple"/></inline-formula> and different ordering scheme for the set of trajectories.</p></sec><sec id="s3"><title>3. Integrable Billiard</title><p>If one considers integrable quantum systems and analyzes them in terms of the NNS distribution of energy levels and spectral rigidity, then in most cases one finds a Poissonian distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x40.png" xlink:type="simple"/></inline-formula> which is reflected also in the behaviour of<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x40.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x41.png" xlink:type="simple"/></inline-formula>. However, there are examples of integrable quantum systems in 2D where the levels are correlated and the level spacing distribution is not given by a Poissonian. Such cases are called non-generic. Berry and Tabor [<xref ref-type="bibr" rid="scirp.58453-ref39">39</xref>] noted as example uncoupled quantum oscillators in 2D. Casati et al. [<xref ref-type="bibr" rid="scirp.58453-ref40">40</xref>] and later Seligman and Verbaarschot [<xref ref-type="bibr" rid="scirp.58453-ref41">41</xref>] showed that also the integrable 2D quantum rectangular incommensurate billiard does not give an uncorrelated Poissonian level spacing distribution. In a recent study of 2D quantum harmonic oscillators Chakrabarti and Hu [<xref ref-type="bibr" rid="scirp.58453-ref42">42</xref>] found in the case of uncoupled oscillators a level spacing distribution displaying a <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x40.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x41.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x42.png" xlink:type="simple"/></inline-formula> peak plus some background. They measured the spectral correlation via the</p><p>spectral rigidity <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x43.png" xlink:type="simple"/></inline-formula> and observed a flat curve for <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x43.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x44.png" xlink:type="simple"/></inline-formula> saturating at<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x43.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x44.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x45.png" xlink:type="simple"/></inline-formula>. Similar results</p><p>were found in the case with weak harmonic coupling, yielding <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x46.png" xlink:type="simple"/></inline-formula> and correlation<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x46.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x47.png" xlink:type="simple"/></inline-formula>. This behaviour is similar to the rigidity of a picket fence (Dirac comb) of equally spaced levels with<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x46.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x47.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x48.png" xlink:type="simple"/></inline-formula>. They conclude that the 2D quantum oscillator system is highly correlated at short and long- range, is regular and very rigid.</p><p>Laprise et al. [<xref ref-type="bibr" rid="scirp.58453-ref36">36</xref>] considered the classical integrable circular billiard and constructed a length matrix from classical trajectories between boundary points located evenly on the billiard wall. They found saturation in the spectral rigidity for large L at <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x49.png" xlink:type="simple"/></inline-formula> which behaves like the rigidity of a Dirac comb<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x49.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x50.png" xlink:type="simple"/></inline-formula>. In conjunction with a correlation coefficient of <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x49.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x50.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x51.png" xlink:type="simple"/></inline-formula> and a level spacing distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x49.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x50.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x51.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x52.png" xlink:type="simple"/></inline-formula> (Dirac comb) this indicates high correlation at short and long range and very rigid behaviour.</p>Rectangular Billiard<p>As example of an integrable classical billiard we consider the 2D rectangular billiard, shown in <xref ref-type="fig" rid="fig1">Figure 1</xref>.</p><p>The shape is determined by the parameters a, b, which were chosen to be <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x53.png" xlink:type="simple"/></inline-formula> and<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x53.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x54.png" xlink:type="simple"/></inline-formula>. The boundary points<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x53.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x54.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x55.png" xlink:type="simple"/></inline-formula>, located in the upper right quarter of the billiard wall, are distributed regularly, with perimeter spacing given by</p><disp-formula id="scirp.58453-formula968"><label>(6)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x56.png"  xlink:type="simple"/></disp-formula><p>For a given pair of boundary points, we found that the behaviour of the number of trajectories versus the number of rebounds is linear (not shown). The error behaviour of trajectories as a function of the number of rebounds has been obtained by taking into account <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x57.png" xlink:type="simple"/></inline-formula> trajectories corresponding to different initial conditions. The error was stable in the regime <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x57.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x58.png" xlink:type="simple"/></inline-formula> and did not increase beyond<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x57.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x58.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x59.png" xlink:type="simple"/></inline-formula>. This is in contrast to an exponential increase found in chaotic billiards.</p><p>The symmetry of the rectangular billiard is mirror symmetry under reflection about the x- and y-axes (with origin at centre of rectangle). The symmetry shows up in the shape of trajectories. For example, a trajectory (1) going from starting point <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x60.png" xlink:type="simple"/></inline-formula> to endpoint <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x60.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x61.png" xlink:type="simple"/></inline-formula> bouncing once from the lower boundary wall has the same shape as the trajectory (2) going from starting point <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x60.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x61.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x62.png" xlink:type="simple"/></inline-formula> to endpoint <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x60.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x61.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x62.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x63.png" xlink:type="simple"/></inline-formula> bouncing once from the lower boundary wall. Trajectory (2) is obtained by a translation of trajectory (1) in x-direction by the amount of perimeter spacing, Equation (6). However, such discrete translations do not form a group (they would form a group if one would consider the rectangular billiard with periodic boundary conditions at all billiard boundary walls, which would map the billiard on a torus, and the symmetry group would then be a rotation group). The length matrix is not exactly a circular matrix either, however, it shares with circular matrices the property of having a number of different matrix elements equal to or less than the rank of the matrix. This property implies strong correlations between length matrix elements, which translates to strong correlations of eigenvalues of the length matrix. To sum up, although there is no exact symmetry group, there are residues of a “broken symmetry</p><fig id="fig1"  position="float"><label><xref ref-type="fig" rid="fig1">Figure 1</xref></label><caption><title> 2D Rectangular billiard. Horizontal length a, vertical length b. Trajectories go from boundary points <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x65.png" xlink:type="simple"/></inline-formula> to <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x65.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x66.png" xlink:type="simple"/></inline-formula> lo- cated in the upper right quarter of the billiard wall</title></caption><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x64.png"/></fig><p>group of discrete translations,” which imply strong correlations among length matrix elements and among eigenvalues of the length matrix. We expect that this will manifest itself in the statistical behaviour in the level spacing distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x67.png" xlink:type="simple"/></inline-formula> and the spectral rigidity<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x67.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x68.png" xlink:type="simple"/></inline-formula>. The results corresponding to the parameters <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x67.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x68.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x69.png" xlink:type="simple"/></inline-formula> and <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x67.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x68.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x69.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x70.png" xlink:type="simple"/></inline-formula> are shown in <xref ref-type="fig" rid="fig2">Figure 2</xref>. <xref ref-type="fig" rid="fig2">Figure 2</xref>(a) represents the distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x67.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x68.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x69.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x70.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x71.png" xlink:type="simple"/></inline-formula> of length matrix elements. Its shape looks quite different from the near-Gaussian shape for chaotic billiards (see below). <xref ref-type="fig" rid="fig2">Figure 2</xref>(b) shows the spectral rigidity <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x67.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x68.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x69.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x70.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x71.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x72.png" xlink:type="simple"/></inline-formula> of the spectrum of the length matrix. It rapidly saturates to a value</p><p><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x73.png" xlink:type="simple"/></inline-formula>, which is close to the value <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x73.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x74.png" xlink:type="simple"/></inline-formula> of an ideal Dirac comb. We have also</p><p>computed the correlation coefficient to obtain<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x75.png" xlink:type="simple"/></inline-formula>. This is consistent also with the NNS level distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x75.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x76.png" xlink:type="simple"/></inline-formula> of the length matrix shown in <xref ref-type="fig" rid="fig2">Figure 2</xref>(c) where one observes <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x75.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x76.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x77.png" xlink:type="simple"/></inline-formula> indicating the behaviour of a Dirac comb. The rectangular billiard turns out to be highly correlated at short and long range, to be very rigid and to be regular.</p><p>This is possibly evidence for universal behaviour in the integrable case. Comparing the behaviour of the rectangular billiard with the circular billiard [<xref ref-type="bibr" rid="scirp.58453-ref36">36</xref>] , we observe that they differ in their symmetry properties. In the circular billiard hopping from one boundary point to its neighbour stands for a group operation. The corre- sponding operation in the rectangular billiard has no group property. However, the resulting strong correlation and spectral rigidity are found to be very similar for both billiards.</p><fig-group id="fig2"><label><xref ref-type="fig" rid="fig2">Figure 2</xref></label><caption><title> Rectangular billiard. (a) Distribution of length matrix elements<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x81.png" xlink:type="simple"/></inline-formula>; (b) Spectral rigidity <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x81.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x82.png" xlink:type="simple"/></inline-formula> of length spectrum; (c) Level spacing distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x81.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x82.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x83.png" xlink:type="simple"/></inline-formula> of length spectrum.</title></caption><fig id ="fig2_1"><label>(b)</label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x78.png"/></fig><fig id ="fig2_2"><label>(c)</label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x79.png"/></fig><fig id ="fig2_3"><label></label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x80.png"/></fig></fig-group></sec><sec id="s4"><title>4. Chaotic Billiards</title><sec id="s4_1"><title>4.1. Sinai Billiard</title><p>For general closed 2D billiards, the mean free path length <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x84.png" xlink:type="simple"/></inline-formula> in between two collisions is given by the billiard geometry via [<xref ref-type="bibr" rid="scirp.58453-ref43">43</xref>]</p><disp-formula id="scirp.58453-formula969"><label>(7)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x85.png"  xlink:type="simple"/></disp-formula><p>where <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x86.png" xlink:type="simple"/></inline-formula> stands for the billiard area and <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x86.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x87.png" xlink:type="simple"/></inline-formula> for the length of the wall. Let us now consider a particle of mass m moving in the 2D Sinai billiard (see <xref ref-type="fig" rid="fig3">Figure 3</xref>(a)). The classical Sinai billiard system is known to be fully chaotic [<xref ref-type="bibr" rid="scirp.58453-ref44">44</xref>] . The billiard is symmetric under mirror operation about x- and y-axes, and about the diagonals of angles <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x86.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x87.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x88.png" xlink:type="simple"/></inline-formula> and<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x86.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x87.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x88.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x89.png" xlink:type="simple"/></inline-formula>, respectively, passing through the centre. In order not to mix different symmetry classes, we consider the billiard with boundary points <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x86.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x87.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x88.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x89.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x90.png" xlink:type="simple"/></inline-formula> located in one eighth of the exterior billiard wall (see <xref ref-type="fig" rid="fig3">Figure 3</xref>(a)). It is important to make sure that the chaotic behaviour comes from the dynamics of the system and not from random location of boundary points. Thus we have located the boundary points in a regular way, with perimeter spacing</p><disp-formula id="scirp.58453-formula970"><label>(8)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x91.png"  xlink:type="simple"/></disp-formula><p>The rule of dynamics is free motion in the interior region and elastic specular collision at the central disc and the exterior square wall. We have classified trajectories using the scheme presented in Section 2. A global</p><fig-group id="fig3"><label><xref ref-type="fig" rid="fig3">Figure 3</xref></label><caption><title> (a) Geometry of 2D Sinai billiard (not to scale). Para- meters: radius of sphere<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x94.png" xlink:type="simple"/></inline-formula>, half-length of box<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x94.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x95.png" xlink:type="simple"/></inline-formula>. Trajectories go from boundary points <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x94.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x95.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x96.png" xlink:type="simple"/></inline-formula> to <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x94.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x95.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x96.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x97.png" xlink:type="simple"/></inline-formula> located in a segment of one eighth of the wall of the billiard; (b) Relative error as function of number of rebounds for 2D Sinai billiard.</title></caption><fig id ="fig3_1"><label>(b)</label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x92.png"/></fig><fig id ="fig3_2"><label></label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x93.png"/></fig></fig-group><p>characteristic feature of chaos is encoded in the number of classical trajectories. For the Sinai billiard we found that the number <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x98.png" xlink:type="simple"/></inline-formula> of trajectories averaged over all pairs of boundary points increases as function of the number of rebounds <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x98.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x99.png" xlink:type="simple"/></inline-formula> like an exponential,</p><disp-formula id="scirp.58453-formula971"><label>(9)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x100.png"  xlink:type="simple"/></disp-formula><p>Such exponential behaviour in chaotic billiards is clearly distinct from the behaviour in integrable billiards, where the number of trajectories increases linearly with the number of rebounds (see rectangular billiard). We</p><p>fixed a value for <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x101.png" xlink:type="simple"/></inline-formula> and then generated an ensemble of length matrices <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x101.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x102.png" xlink:type="simple"/></inline-formula> corresponding</p><p>to all possible trajectory indices<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x103.png" xlink:type="simple"/></inline-formula>. In general one expects that chaotic behaviour develops with increasing<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x103.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x104.png" xlink:type="simple"/></inline-formula>.</p><p>In order to make sure that the chaotic behaviour is not due to numerical noise, we estimated the numerical error <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x105.png" xlink:type="simple"/></inline-formula> by following a trajectory from a given starting point <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x105.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x106.png" xlink:type="simple"/></inline-formula> until it carried out <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x105.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x106.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x107.png" xlink:type="simple"/></inline-formula> rebounds, then following the trajectory in time reversed direction to arrive after another <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x105.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x106.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x107.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x108.png" xlink:type="simple"/></inline-formula> at some<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x105.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x106.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x107.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x108.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x109.png" xlink:type="simple"/></inline-formula>. We observed that the error behaviour has two regimes. For <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x105.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x106.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x107.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x108.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x109.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x110.png" xlink:type="simple"/></inline-formula> it shows an exponential increase, following on average the rule</p><disp-formula id="scirp.58453-formula972"><label>(10)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x111.png"  xlink:type="simple"/></disp-formula><p>For <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x112.png" xlink:type="simple"/></inline-formula> saturation is reached (the relative error is in the order of one). Such exponential behaviour (see <xref ref-type="fig" rid="fig3">Figure 3</xref>(b)) is related to the largest positive Lyapunov exponent of the system and thus represents a global characteristic of a chaotic system. Like the exponential behaviour of number of trajectories, Equation (9), also the exponential error behaviour distinguishes between chaotic billiards and integrable billiards.</p><p>We found a relative error of about <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x113.png" xlink:type="simple"/></inline-formula> for<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x113.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x114.png" xlink:type="simple"/></inline-formula>. After unfolding the spectra we retain 8 significant digits. We used the technique of Gaussian broadening [<xref ref-type="bibr" rid="scirp.58453-ref13">13</xref>] [<xref ref-type="bibr" rid="scirp.58453-ref45">45</xref>] to unfold the raw spectrum. In this method there is a free parameter, which we tuned to reproduce the auto-correlation coefficient (<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x113.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x114.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x115.png" xlink:type="simple"/></inline-formula>for Wigner distribution [<xref ref-type="bibr" rid="scirp.58453-ref8">8</xref>] [<xref ref-type="bibr" rid="scirp.58453-ref46">46</xref>] ). For a given trajectory index <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x113.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x114.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x115.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x116.png" xlink:type="simple"/></inline-formula> we computed a level spacing distribution. Afterwards we superimposed the spectra corresponding to different trajectory indices<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x113.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x114.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x115.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x116.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x117.png" xlink:type="simple"/></inline-formula>. For <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x113.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x114.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x115.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x116.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x117.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x118.png" xlink:type="simple"/></inline-formula> the number of trajectories is quite large<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x113.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x114.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x115.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x116.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x117.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x118.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x119.png" xlink:type="simple"/></inline-formula>. In order to limit computational cost, we have restricted ourselves by considering smaller subsets of length matrices. It turns out that <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x113.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x114.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x115.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x116.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x117.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x118.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x119.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x120.png" xlink:type="simple"/></inline-formula> gives a reasonably good statistics. Note that, in this way, we don’t consider all trajectories and have to order them arbitrarily as explained in Section 2. Therefore, we verified that the resulting distributions are independent of the particular subset of trajectories and ordering used within statistical errors. The resulting level spacing distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x113.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x114.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x115.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x116.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x117.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x118.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x119.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x120.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x121.png" xlink:type="simple"/></inline-formula> of length eigenvalues and the spectral rigidity<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x113.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x114.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x115.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x116.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x117.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x118.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x119.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x120.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x121.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x122.png" xlink:type="simple"/></inline-formula>, corresponding to <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x113.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x114.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x115.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x116.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x117.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x118.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x119.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x120.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x121.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x122.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x123.png" xlink:type="simple"/></inline-formula> boundary points, <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x113.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x114.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x115.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x116.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x117.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x118.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x119.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x120.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x121.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x122.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x123.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x124.png" xlink:type="simple"/></inline-formula>rebounds and <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x113.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x114.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x115.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x116.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x117.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x118.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x119.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x120.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x121.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x122.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x123.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x124.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x125.png" xlink:type="simple"/></inline-formula> trajectories, are shown in <xref ref-type="fig" rid="fig4">Figure 4</xref>(b) &amp; <xref ref-type="fig" rid="fig4">Figure 4</xref>(c). The results show a Wigner distribution for<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x113.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x114.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x115.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x116.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x117.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x118.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x119.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x120.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x121.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x122.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x123.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x124.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x125.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x126.png" xlink:type="simple"/></inline-formula>, and are consistent with GOE behaviour also for<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x113.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x114.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x115.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x116.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x117.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x118.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x119.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x120.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x121.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x122.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x123.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x124.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x125.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x126.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x127.png" xlink:type="simple"/></inline-formula>. As in the quantum Sinai billiard, the level spacing distribution has been shown to give a Wigner distribution [<xref ref-type="bibr" rid="scirp.58453-ref7">7</xref>] .</p><p>The histogram of the length matrix elements itself is shown in <xref ref-type="fig" rid="fig4">Figure 4</xref>(a). The distribution looks close to a Gaussian. Determining if it is a pure Gaussian, is a question physically relevant for the following reason: If the distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x128.png" xlink:type="simple"/></inline-formula> is a Gaussian, then the matrix elements obey GOE statistics. Then RMT [<xref ref-type="bibr" rid="scirp.58453-ref6">6</xref>] implies that the level fluctuation statistics <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x128.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x129.png" xlink:type="simple"/></inline-formula> and the spectral rigidity <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x128.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x129.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x130.png" xlink:type="simple"/></inline-formula> follow GOE statistics (which seems consistent with the numerical results shown in <xref ref-type="fig" rid="fig4">Figure 4</xref>(b) &amp; <xref ref-type="fig" rid="fig4">Figure 4</xref>(c). We pointed out above that the random walk model gives a Gaussian distribution for the histogram of the length matrix elements<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x128.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x129.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x130.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x131.png" xlink:type="simple"/></inline-formula>. The random walk model is mathematically a Markov chain, i.e., the length of each piece of straight path in between subsequent collisions is given by a random number. Two subsequent random numbers are statistically inde- pendent. On the other hand, the chaotic Sinai billiard is a deterministic system, and the length of two subsequent pieces of straight trajectory are not independent. Hence it is plausible that the distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x128.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x129.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x130.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x131.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x132.png" xlink:type="simple"/></inline-formula> of length matrix elements for the Sinai billiard is not given by an exact Gaussian.</p><p>Mathematical note. The BGS-conjecture does not state that the matrix elements of a quantum Hamiltonian must be distributed like a GOE ensemble. The conjecture rather only says that the statistical fluctuations of the eigenvalue spacings obtained from the quantum Hamiltonian are the same as those from a GOE ensemble, giving a Wignerian distribution. In other words, it is possible that the matrix elements of the quantum Hamil- tonian be distributed quite differently from a Gaussian and that its level spacing distribution be nevertheless Wignerian.</p><fig-group id="fig4"><label><xref ref-type="fig" rid="fig4">Figure 4</xref></label><caption><title> 2D Sinai billiard.<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x136.png" xlink:type="simple"/></inline-formula>. (a) Distribution of length matrix elements (after subtracting mean<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x136.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x137.png" xlink:type="simple"/></inline-formula>); (b) Level spacing distribution from length matrix<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x136.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x137.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x138.png" xlink:type="simple"/></inline-formula>; (c) Spectral rigidity.</title></caption><fig id ="fig4_1"><label>(b)</label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x133.png"/></fig><fig id ="fig4_2"><label> (c)</label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x134.png"/></fig><fig id ="fig4_3"><label></label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x135.png"/></fig></fig-group><p>Such a situation, where the distribution of matrix elements is not GOE, but the level fluctuation statistics is GOE, occurs in nuclear physics. An example is the distribution of the Hamiltonian matrix elements obtained from nuclear shell model calculations [<xref ref-type="bibr" rid="scirp.58453-ref12">12</xref>] . In this model, there are vanishing Hamiltonian matrix elements. This implies that the number of independent matrix elements is much smaller than in a random matrix of the same size. However, Brody et al. [<xref ref-type="bibr" rid="scirp.58453-ref47">47</xref>] showed that the 2-body residual interaction in the shell model yields matrix elements of random character following a Gaussian distribution. In particular, they showed that spectral fluctuation properties from such ensembles with orthogonal symmetry are identical to those from GOE. That implies that GOE is meaningful to predict spectral fluctuation properties of nuclei governed by 2-body interactions, though the Hamiltanian does not follow a Gaussian distribution.</p></sec><sec id="s4_2"><title>4.2. Bunimovich Stadium Billiard</title><p>Let us consider the 2D Bunimovich stadium billiard with semi-axes a and b (<xref ref-type="fig" rid="fig5">Figure 5</xref>). The billiard is known to be fully chaotic [<xref ref-type="bibr" rid="scirp.58453-ref48">48</xref>] [<xref ref-type="bibr" rid="scirp.58453-ref49">49</xref>] . The billiard is symmetric under mirror operation about x- and y-axes. In order not to mix different symmetry classes, we consider the billiard with boundary points <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x139.png" xlink:type="simple"/></inline-formula> located in one quarter of the billiard wall. The quarter perimeter has the length<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x139.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x140.png" xlink:type="simple"/></inline-formula>. The nodes <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x139.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x140.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x141.png" xlink:type="simple"/></inline-formula> are equidistantly distributed on the quarter perimeter of the stadium, with perimeter spacing given by</p><disp-formula id="scirp.58453-formula973"><label>(11)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x142.png"  xlink:type="simple"/></disp-formula><p>The rule of dynamics is free motion in the interior region and elastic specular collision at the exterior square wall. For a given pair of boundary points, the number of trajectories <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x143.png" xlink:type="simple"/></inline-formula> connecting these points increases on average with the number of rebounds exponentially, according to</p><disp-formula id="scirp.58453-formula974"><label>(12)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x144.png"  xlink:type="simple"/></disp-formula><p>Such behaviour is qualitatively similar to that found in the Sinai billiard. We also measured the numerical</p><fig id="fig5"  position="float"><label><xref ref-type="fig" rid="fig5">Figure 5</xref></label><caption><title> 2D stadium billiard. Trajectories go from boundary points <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x146.png" xlink:type="simple"/></inline-formula> to <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x146.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x147.png" xlink:type="simple"/></inline-formula> located in a segment of one quarter of the billiard wall</title></caption><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x145.png"/></fig><p>error following the method used in the Sinai billiard. Likewise, we found a regime of exponential behaviour followed by a regime of saturation (not shown). On average the exponential increase is given by</p><disp-formula id="scirp.58453-formula975"><label>(13)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x148.png"  xlink:type="simple"/></disp-formula><p>We have classified trajectories by the the number of rebounds<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x149.png" xlink:type="simple"/></inline-formula>, which was kept fixed. In this way, we</p><p>generate an ensemble of length matrices <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x150.png" xlink:type="simple"/></inline-formula> corresponding to all possible trajectory indices<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x150.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x151.png" xlink:type="simple"/></inline-formula>.</p><p>For each trajectory index <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x152.png" xlink:type="simple"/></inline-formula> we computed a level spacing distribution. Afterwards we superimposed the spectra coming from a smaller subset of trajectory indices <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x152.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x153.png" xlink:type="simple"/></inline-formula> (like we did for the Sinai billiard). The resulting level spacing distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x152.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x153.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x154.png" xlink:type="simple"/></inline-formula> of length eigenvalues and the spectral rigidity <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x152.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x153.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x154.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x155.png" xlink:type="simple"/></inline-formula> are shown in <xref ref-type="fig" rid="fig6">Figure 6</xref>(b) &amp; <xref ref-type="fig" rid="fig6">Figure 6</xref>(c). The results are consistent with a Wigner distribution, i.e. GOE behaviour. The histogram of the length matrix elements <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x152.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x153.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x154.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x155.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x156.png" xlink:type="simple"/></inline-formula> shown in <xref ref-type="fig" rid="fig6">Figure 6</xref>(a) looks close to a Gaussian.</p><p>In order to see if the observed Wigner distribution in the level spacing distribution depends on the statistical method of averaging over several trajectories (i.e. length matrices<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x157.png" xlink:type="simple"/></inline-formula>) we have tested an alternative, where we averaged over different stadium geometries. All stadium billiards are fully chaotic, and if the Wigner behaviour is universal, this should show up for any stadium geometry. Superimposing spectra corresponding to different stadium shapes should improve statistics. In their paper on quantum chaos in nuclear physics, Bohigas et al. [<xref ref-type="bibr" rid="scirp.58453-ref50">50</xref>] have analyzed spectra of a series of different heavy nuclei (but having the same quantum numbers) and showed that their level fluctuation statistics agrees with the prediction of RMT. These different nuclei correspond to different potentials. In analogy to that we considered here length spectra corresponding to different stadium shapes. The stadium wall represents a curve where the potential jumps from zero to infinity. Hence different stadium shapes correspond to different potentials. By superimposing spectra from different stadium shapes we obtained results displayed in <xref ref-type="fig" rid="fig7">Figure 7</xref>. Within statistical errors the results are equivalent to those obtained by superimposing spectra from different trajectories (<xref ref-type="fig" rid="fig6">Figure 6</xref>).</p></sec></sec><sec id="s5"><title>5. Universality in Chaotic Billiards</title><p>The numerical experiments with chaotic billiards investigated above led us to the following observations: 1) For a given pair of boundary points<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x158.png" xlink:type="simple"/></inline-formula>, the number of classical trajectories <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x158.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x159.png" xlink:type="simple"/></inline-formula> shows exponential behaviour as function of<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x158.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x159.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x160.png" xlink:type="simple"/></inline-formula>. 2) For a given pair of boundary points, the numerical error <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x158.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x159.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x160.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x161.png" xlink:type="simple"/></inline-formula> shows exponential behaviour as function of the number of rebounds<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x158.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x159.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x160.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x161.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x162.png" xlink:type="simple"/></inline-formula>. 3) The NNS level fluctuation distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x158.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x159.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x160.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x161.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x162.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x163.png" xlink:type="simple"/></inline-formula> obtained from eigenvalues of the length matrix <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x158.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x159.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x160.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x161.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x162.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x163.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x164.png" xlink:type="simple"/></inline-formula> shows a Wignerian distribution. This is consistent with GOE behaviour predicted by RMT. 4) The spectral rigidity <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x158.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x159.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x160.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x161.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x162.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x163.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x164.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x165.png" xlink:type="simple"/></inline-formula> obtained from the spectrum of length matrix <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x158.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x159.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x160.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x161.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x162.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x163.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x164.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x165.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x166.png" xlink:type="simple"/></inline-formula> is also found consistent with GOE behaviour predicted by RMT. 5) The distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x158.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x159.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x160.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x161.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x162.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x163.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x164.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x165.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x166.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x167.png" xlink:type="simple"/></inline-formula> of length matrix elements <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x158.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x159.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x160.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x161.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x162.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x163.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x164.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x165.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x166.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x167.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x168.png" xlink:type="simple"/></inline-formula> is found to be close to a Gaussian.</p><p>The leading Gaussian behaviour in the distribtion <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x169.png" xlink:type="simple"/></inline-formula> is possibly universal. How can we understand such behaviour of <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x169.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x170.png" xlink:type="simple"/></inline-formula> in chaotic billiards? The Sinai billiard is equivalent to a system where the billiard ball moves in an open system of equal spherical discs located on a 2D rectangular regular grid, known as the 2D (diluted) Lorentz gas model. The billiard ball alternates between free motion and collisions with the circular</p><fig-group id="fig6"><label><xref ref-type="fig" rid="fig6">Figure 6</xref></label><caption><title> 2D stadium billiard. Average over trajectories. Geometry parameters<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x174.png" xlink:type="simple"/></inline-formula>. Boundary points on quarter of wall<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x174.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x175.png" xlink:type="simple"/></inline-formula>. (a) Distribution of length matrix elements (after subtraction of mean<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x174.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x175.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x176.png" xlink:type="simple"/></inline-formula>).<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x174.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x175.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x176.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x177.png" xlink:type="simple"/></inline-formula>; (b) Level spacing distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x174.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x175.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x176.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x177.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x178.png" xlink:type="simple"/></inline-formula> from length matrix<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x174.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x175.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x176.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x177.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x178.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x179.png" xlink:type="simple"/></inline-formula>.<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x174.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x175.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x176.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x177.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x178.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x179.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x180.png" xlink:type="simple"/></inline-formula>. Average over <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x174.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x175.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x176.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x177.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x178.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x179.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x180.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x181.png" xlink:type="simple"/></inline-formula> trajectories; (c) Spectral rigidity<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x174.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x175.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x176.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x177.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x178.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x179.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x180.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x181.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x182.png" xlink:type="simple"/></inline-formula>, same parameters as (b).</title></caption><fig id ="fig6_1"><label>(b)</label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x171.png"/></fig><fig id ="fig6_2"><label>(c)</label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x172.png"/></fig><fig id ="fig6_3"><label></label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x173.png"/></fig></fig-group><p>discs. There are two types of 2D periodic Lorentz gases (or Sinai billiards on a torus): One has a finite horizon, where free paths between collisions are bounded (the scatterers are sufficiently dense to block every direction of motion. The other type has an infinite horizon, where the particle can move indefinitely without collision with</p><fig-group id="fig7"><label><xref ref-type="fig" rid="fig7">Figure 7</xref></label><caption><title> 2D stadium billiard. Average over shapes of stadium. (a) Distribution of length matrix elements.<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x186.png" xlink:type="simple"/></inline-formula>; (b) Level spacing distribution from length matrix<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x186.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x187.png" xlink:type="simple"/></inline-formula>; (c) Spec- tral rigidity<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x186.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x187.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x188.png" xlink:type="simple"/></inline-formula>.</title></caption><fig id ="fig7_1"><label>(b)</label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x183.png"/></fig><fig id ="fig7_2"><label>(c)</label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x184.png"/></fig><fig id ="fig7_3"><label></label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x185.png"/></fig></fig-group><p>any disc. The Sinai billiard investigated above (see <xref ref-type="fig" rid="fig3">Figure 3</xref>(a)) belongs to this class. The CLT, the existence of finite diffusion coefficient and the convergence to Brownian motion were proven only for the periodic Lorentz gas with finite horizon [<xref ref-type="bibr" rid="scirp.58453-ref21">21</xref>] [<xref ref-type="bibr" rid="scirp.58453-ref23">23</xref>] . In the case of infinite horizon, the CLT has not been proven. Moreover, in this case the diffusion coefficient is infinite and there is no convergence to Brownian motion [<xref ref-type="bibr" rid="scirp.58453-ref51">51</xref>] .</p><p>Actually, for the periodic Lorentz gas with finite horizon it can be shown rigorously that the distribution of length of trajectories becomes a Gaussian distribution in the limit of many bounces. This holds when the initial points of trajectories are distributed randomly on the billiard boundary. Then Chernov and Markarian [<xref ref-type="bibr" rid="scirp.58453-ref52">52</xref>] prove the following result</p><disp-formula id="scirp.58453-formula976"><label>(14)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x189.png"  xlink:type="simple"/></disp-formula><p>for all<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x190.png" xlink:type="simple"/></inline-formula>. Here <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x190.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x191.png" xlink:type="simple"/></inline-formula> denotes the return function and</p><disp-formula id="scirp.58453-formula977"><label>(15)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x192.png"  xlink:type="simple"/></disp-formula><p>is the time of the n-th collision. <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x193.png" xlink:type="simple"/></inline-formula>is the variance of the random variable depending on the observable f, which here is the return function<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x193.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x194.png" xlink:type="simple"/></inline-formula>. <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x193.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x194.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x195.png" xlink:type="simple"/></inline-formula>is the collision map and M is the collision space (phase space of billiard map). Thus the distribution of the travel time until the n-th collision obeys the CLT, and hence converges in distribution to a normal, i.e. Gaussian distribution in the limit where the number of collisions goes to infinity. In the case where the billiard particle moves with constant speed u, this means also the length of trajectory <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x193.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x194.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x195.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x196.png" xlink:type="simple"/></inline-formula> obeys the CLT, and the length distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x193.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x194.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x195.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x196.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x197.png" xlink:type="simple"/></inline-formula> tends to a normal Gaussian dis- tribution for<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x193.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x194.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x195.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x196.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x197.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x198.png" xlink:type="simple"/></inline-formula>.</p><p>This result seems to support our numerical findings of a (near) Gaussian distribution of length of trajectories. However, the scenario where the above mathematical result holds and the scenario of our numerical study differ in two respects, namely in the distribution of boundary points and in the horizon of billiard. For the purpose of statistical analysis in terms of RMT we are interested in the distribution<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x199.png" xlink:type="simple"/></inline-formula>, which corresponds to the case where the billiard particle starts from and arrives at boundary positions taken from a discrete set of boundary points, which is different from an initial random set of boundary points. Nevertheless, it seems plausible that a similar mathematical result may hold also for the discrete set of initial and final boundary points, such that <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x199.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x200.png" xlink:type="simple"/></inline-formula> tends to a Gaussian. Second, the mathematical result holds for the case of finite horizon, while the Sinai billiard in our numerical study has an infinite horizon. At a first glance, it looks puzzling in absence of strong mathematical results (for infinite horizon) that our numerical results nevertheless show (near) Gaussian behaviour for the length distribution. A possible explanation is this: The choice of the discrete set of regularly distributed boundary points selects preferentially trajectories which often bounce at the central disc, while trajectories with infinite horizon (no bounces at disc) are avoided. This is supported also by numerical evidence that the distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x199.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x200.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x201.png" xlink:type="simple"/></inline-formula> of the Sinai billiard (<xref ref-type="fig" rid="fig4">Figure 4</xref>(a)) and that of the rectangular billiard viewed as Sinai billiard with central disc of radius zero (<xref ref-type="fig" rid="fig2">Figure 2</xref>(a)) are quite different.</p><p>In the case of the stadium billiard, B&#225;lint and Gou&#235;zel [<xref ref-type="bibr" rid="scirp.58453-ref53">53</xref>] have shown that a limit theorem also holds. They proved that the limit distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x202.png" xlink:type="simple"/></inline-formula> is Gaussian, however, the scaling factor is not given by the standard expression<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x202.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x203.png" xlink:type="simple"/></inline-formula>, but rather by<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x202.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x203.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x204.png" xlink:type="simple"/></inline-formula>. As consequence, there is no finite standard diffusion coefficient and no convergence to Brownian motion. B&#225;lint and Gou&#235;zel prove that the distribution of length of trajectories, tends to a gaussian distribution in the limit where the number of collisions n goes to infinity. Like in the Lorentz gas, this corresponds to a random distribution of initial points on the boundary. This is consistent with our numerical results of the stadium billiard which show in <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x202.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x203.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x204.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x205.png" xlink:type="simple"/></inline-formula> small deviations from a Gaussian comparable in magnitude with those in the Sinai model. They are likely due to the small number of bounces.</p><p>Let us suppose that <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x206.png" xlink:type="simple"/></inline-formula> does tend to a Gaussian. As a consequence, in this limit (and after suitable normalization) the length matrix <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x206.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x207.png" xlink:type="simple"/></inline-formula> itself will become a GOE matrix [<xref ref-type="bibr" rid="scirp.58453-ref11">11</xref>] . This is sufficient to guarantee in this limit that the level fluctuations <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x206.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x207.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x208.png" xlink:type="simple"/></inline-formula> and spectral rigidity <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x206.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x207.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x208.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x209.png" xlink:type="simple"/></inline-formula> obtained from the matrix <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x206.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x207.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x208.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x209.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x210.png" xlink:type="simple"/></inline-formula> show GOE behaviour [<xref ref-type="bibr" rid="scirp.58453-ref6">6</xref>] . This does establish universal behaviour in the limit of many bounces in the case of the rectangular Lorentz gas/Sinai billiard as well as the Bunimovich stadium billiard.</p><p>Now we want to address the following questions:</p><p>1) Concerning universality observed in classical chaotic billiards, what are the underlying physical principles? We will give a heuristic description-not a rigorous derivation-of the physical principles leading to the pheno- menon of universality. Let us consider chaotic billiards in the regime of macroscopic times, i.e., when the billiard particle does a large number of bounces. Consider a trajectory carrying out <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x211.png" xlink:type="simple"/></inline-formula> bounces. Because the system is a classically deterministic system, each segment of trajectory between two subsequent bounces depends on and is completely determined by the preceeding segment of trajectory. However, if one computes the correlator between trajectory segment n and trajectory segment<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x211.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x212.png" xlink:type="simple"/></inline-formula>, such correlation tends to zero, when k becomes large (see below equations (16) and (17), and reference [<xref ref-type="bibr" rid="scirp.58453-ref52">52</xref>] ). This effect is due to the dynamics of classical chaos (positive Lyapunov exponent). Thus two segments of the same trajectory, which are sufficiently distant in terms of travel time or number of intermediate bounces, will become statistically independent. This makes the system similar to a random walk system, where any two segments of a trajectory are statistically independent. To see this, consider trajectories of the chaotic billiard composed of N segments (<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x211.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x212.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x213.png" xlink:type="simple"/></inline-formula>bounces), and choose<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x211.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x212.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x213.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x214.png" xlink:type="simple"/></inline-formula>. Then we can consider all segments <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x211.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x212.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x213.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x214.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x215.png" xlink:type="simple"/></inline-formula> with <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x211.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x212.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x213.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x214.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x215.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x216.png" xlink:type="simple"/></inline-formula> to be statistically independent. Consequently, for <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x211.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x212.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x213.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x214.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x215.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x216.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x217.png" xlink:type="simple"/></inline-formula> almost all segments are statistically independent from each other and hence the system will statistically behave similar to a random walk system. This means in the regime of a large number of rebounds that the system looses its memory of the detailed underlying laws of physics. It becomes universal. Such universality manifests itself in the statistical observable of distribution of length of trajecories <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x211.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x212.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x213.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x214.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x215.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x216.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x217.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x218.png" xlink:type="simple"/></inline-formula> which for all chaotic billiards studied here shows a near-Gaussian character (like the random walk system).</p><p>2) What is the physical significance of such universality? If one considers classically chaotic billiards in the regime of macroscopic times, where <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x219.png" xlink:type="simple"/></inline-formula> denotes the matrix of length of trajectories, and if one considers as statistical observable the distribution of length of trajectories<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x219.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x220.png" xlink:type="simple"/></inline-formula>, then <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x219.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x220.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x221.png" xlink:type="simple"/></inline-formula> will asymptotically approach a Gaussian and <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x219.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x220.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x221.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x222.png" xlink:type="simple"/></inline-formula> will asymptotically become a random matrix from a Gaussian Orthogonal Ensemble (GOE). Hence the level spacing distribution of the length matrix <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x219.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x220.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x221.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x222.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x223.png" xlink:type="simple"/></inline-formula> will be described by GOE random matrix statistics. However, one should be careful. The matrix of length <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x219.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x220.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x221.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x222.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x223.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x224.png" xlink:type="simple"/></inline-formula> has not lost all information on the physics of the underlying system. For instance, the variance of length <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x219.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x220.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x221.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x222.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x223.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x224.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x225.png" xlink:type="simple"/></inline-formula> turns out to behave differently in the Sinai billiard (on a torus) in the finite horizon regime, scaling like n, in contrast to the stadium billiard, where it scales like<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x219.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x220.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x221.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x222.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x223.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x224.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x225.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x226.png" xlink:type="simple"/></inline-formula>. Statistical behaviour depends on the particular observable considered. Thus using the length marix<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x219.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x220.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x221.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x222.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x223.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x224.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x225.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x226.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x227.png" xlink:type="simple"/></inline-formula>, which via <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x219.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x220.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x221.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x222.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x223.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x224.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x225.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x226.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x227.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x228.png" xlink:type="simple"/></inline-formula> and <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x219.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x220.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x221.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x222.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x223.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x224.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x225.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x226.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x227.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x228.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x229.png" xlink:type="simple"/></inline-formula> displays universal behaviour, we can compute physical observables, e.g., being related to<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x219.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x220.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x221.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x222.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x223.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x224.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x225.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x226.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x227.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x228.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x229.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x230.png" xlink:type="simple"/></inline-formula>. Such observable yields transport properties, in particular the diffusion coefficient. Results of transport properties are presented in Section 6.</p><p>3) Are there connections between universality and physical quantities which are easily observed in real systems? In particular, is such universality related to transport properties of the classical system? The answer is yes, and we will show in Section 6 how a transport coefficient can be obtained from the length matrix <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x231.png" xlink:type="simple"/></inline-formula> in the regime of universality.</p><p>Moreover, the behaviour of the chaotic billiard systems when approaching the regime of universality is characterized by laws specific for the particular billiard system. As example consider the decay laws of correla- tion functions. Consider a billiard trajectory and consider as observable f the segment of trajectory from n to<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x232.png" xlink:type="simple"/></inline-formula>, while g denotes the segment from <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x232.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x233.png" xlink:type="simple"/></inline-formula> to<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x232.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x233.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x234.png" xlink:type="simple"/></inline-formula>. Then the correlation function between observables f and g for many billiards shows either an exponential fall-off behaviour [<xref ref-type="bibr" rid="scirp.58453-ref52">52</xref>]</p><disp-formula id="scirp.58453-formula978"><label>(16)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x235.png"  xlink:type="simple"/></disp-formula><p>or polynomial fall off behaviour</p><disp-formula id="scirp.58453-formula979"><label>(17)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x236.png"  xlink:type="simple"/></disp-formula><p>where A and B are constants, <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x237.png" xlink:type="simple"/></inline-formula>and the exponent <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x237.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x238.png" xlink:type="simple"/></inline-formula> is specific for the particular billiard. For example, the triangular Lorentz gas as well as the Bunimovich stadium billiard obey exponential falloff behaviour [<xref ref-type="bibr" rid="scirp.58453-ref52">52</xref>] .</p><p>Here we suggest for chaotic billiard systems that the approach towards universality (i.e., increasing the number of rebounds) contains further physical information characteristic for the system. In particular, we expect that the distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x239.png" xlink:type="simple"/></inline-formula> approaches a Gaussian,</p><disp-formula id="scirp.58453-formula980"><label>(18)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x240.png"  xlink:type="simple"/></disp-formula><p>for<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x241.png" xlink:type="simple"/></inline-formula>, where the asymptotic approach is characterized by an exponent characteristic for the particular billiard.</p><p>4) Are those universality properties related to thermodynamical observables? If we consider the chaotic billiards consisting of a single particle moving in a rigid environment of scatterers, then it does not make sense to talk about thermodynamics. Thus for systems considered in this work the answer is no. However, if one considers billiards of many particles, then thermodynamics (as a function of temperature) will influence the dynamics. We shall defer the study of such effects on universality to future work.</p><p>5) Is such universality related to spectral statistics of the corresponding quantum system, i.e., what is the relation between universality in classical chaotic systems and universality in chaotic quantum systems, as defined via the Bohigas-Giannoni-Schmit conjecture [<xref ref-type="bibr" rid="scirp.58453-ref7">7</xref>] ? This is a very interesting question, for which we do not have an answer. The finding that universality properties exist in both, classical and quantum chaos, may be a hint that such a relation actually exists. On the other hand, for universality in quantum chaos in the semi- classical regime, periodic orbits play a crucial role (via Gutzwillers trace formula). In contrast, in our study of chaotic classical billiards universality is captured in the length matrix of bouncing, zig-zag, non-periodic trajectories. In order to address such question further, we suggest to consider the path integral relation (in Euclidean, i.e. imaginary time)</p><disp-formula id="scirp.58453-formula981"><label>(19)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x242.png"  xlink:type="simple"/></disp-formula><p>which relates the quantum Hamltonian H to the classical Euclidean action<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x243.png" xlink:type="simple"/></inline-formula>. For billiards, recall that the length of a trajectory is essentially equivalent to its action. Thus spectral properties of the quantum Hamiltonian may indeed be related to the spectrum of the classical action.</p></sec><sec id="s6"><title>6. Transport Properties from Length of Trajectories</title><p>Above we have shown for the Bunimovich stadium billiard and for the Sinai billiard that they display universality properties via the statistical behaviour of the matrix of length of trajectories. Here we will show that such universal behaviour is related to relevant physical quantities. In particular we will extract transport properties from the length matrix (note the analogy to computation of transport properties in quantum chaos in semi-classical regime via Gutzwiller formula). As examples we consider the stadium billiard.</p><p>In systems for which the CLT is verified, the diffusive character is manifested by a linear relation between the time of travel and the variance of position. The diffusion coefficient d (in 2 dimensions) is given by the Einstein relation</p><disp-formula id="scirp.58453-formula982"><label>(20)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x244.png"  xlink:type="simple"/></disp-formula><p>where <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x245.png" xlink:type="simple"/></inline-formula> denotes the time of travel and <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x245.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x246.png" xlink:type="simple"/></inline-formula> denotes the variance of the position. A similar linear relation between the time of travel and the variance of the length of trajecories can be demonstrated for the periodic triangular Lorentz gas with finite horizon, verifying the CLT (results not shown here):</p><disp-formula id="scirp.58453-formula983"><label>(21)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x247.png"  xlink:type="simple"/></disp-formula><p>where travel time <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x248.png" xlink:type="simple"/></inline-formula> is related to mean trajectory length of n collisions <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x248.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x249.png" xlink:type="simple"/></inline-formula> and velocity u via<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x248.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x249.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x250.png" xlink:type="simple"/></inline-formula>.</p><p>Based on this approach, we define a diffusion coefficient with respect to the variable<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x251.png" xlink:type="simple"/></inline-formula>:</p><disp-formula id="scirp.58453-formula984"><label>(22)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x252.png"  xlink:type="simple"/></disp-formula><p>for the Bunmovich stadium billiard, which is a chaotic system with concave repeller/scatterer. We have chosen to analyze such system, and compute its transport properties because it reveals a very interesting non-standard diffusion behaviour. In a numerical modelling study of the stadium billiard, Borgognoni et al. [<xref ref-type="bibr" rid="scirp.58453-ref54">54</xref>] have studied its diffusion behaviour by testing if a linear relation holds between the time of travel and the variance of angular momentum. They found that a linear relation fits quite well their data and computed the diffusion coefficients as ratio of variance of angular momentum and time of travel (or number of bounces). However, in 2006, B&#225;lint and Gou&#235;zel [<xref ref-type="bibr" rid="scirp.58453-ref53">53</xref>] proved rigorously for the stadium billiard that a “non-standard” limit theorem holds, where the width does not follow the usual <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x253.png" xlink:type="simple"/></inline-formula> as in Equation (22), but rather follows an anomal <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x253.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x254.png" xlink:type="simple"/></inline-formula> law. As consequence, the system does not converge to Brownian motion in the limit of many bounces, and the standard diffusion coefficient does not exist.</p><p>We carried out numerical simulations using trajectories of the length matrix <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x255.png" xlink:type="simple"/></inline-formula> and calculated the total time of travel <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x255.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x256.png" xlink:type="simple"/></inline-formula> and the variance of length<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x255.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x256.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x257.png" xlink:type="simple"/></inline-formula>. According to reference [<xref ref-type="bibr" rid="scirp.58453-ref53">53</xref>] , one expects that the variance should scale like the travel time with a logarithmic correction</p><disp-formula id="scirp.58453-formula985"><label>(23)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x258.png"  xlink:type="simple"/></disp-formula><p>We define the diffusion coefficient by</p><disp-formula id="scirp.58453-formula986"><label>(24)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x259.png"  xlink:type="simple"/></disp-formula><p>We have done statistical tests of <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x260.png" xlink:type="simple"/></inline-formula> comparing a fit linear in n with a fit linear in<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x260.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x261.png" xlink:type="simple"/></inline-formula>. The results are shown in <xref ref-type="fig" rid="fig8">Figure 8</xref>. Making fits of the form</p><disp-formula id="scirp.58453-formula987"><graphic  xlink:href="http://html.scirp.org/file/25-7402611x262.png"  xlink:type="simple"/></disp-formula><disp-formula id="scirp.58453-formula988"><label>(25)</label><graphic position="anchor" xlink:href="http://html.scirp.org/file/25-7402611x263.png"  xlink:type="simple"/></disp-formula><p>we obtained for the linear fit <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x264.png" xlink:type="simple"/></inline-formula> with an error of 2.8 (max) and <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x264.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x265.png" xlink:type="simple"/></inline-formula> (mean) shown in</p><fig-group id="fig8"><label><xref ref-type="fig" rid="fig8">Figure 8</xref></label><caption><title> Anomalous diffusion in stadium billiard. (a) Variance of <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x269.png" xlink:type="simple"/></inline-formula> linear fit in n; (b) Variance of <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x269.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x270.png" xlink:type="simple"/></inline-formula> fit including loga- rithmic term<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x269.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x270.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x271.png" xlink:type="simple"/></inline-formula>; (c) Diffusion constant from including logarithmic term.</title></caption><fig id ="fig8_1"><label>(b)</label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x266.png"/></fig><fig id ="fig8_2"><label>(c)</label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x267.png"/></fig><fig id ="fig8_3"><label></label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/25-7402611x268.png"/></fig></fig-group><p><xref ref-type="fig" rid="fig8">Figure 8</xref>(a), compared to the fit with logarithmic correction giving <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x272.png" xlink:type="simple"/></inline-formula> with an error of <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x272.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x273.png" xlink:type="simple"/></inline-formula> (max) and <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x272.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x273.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x274.png" xlink:type="simple"/></inline-formula> (mean) shown in <xref ref-type="fig" rid="fig8">Figure 8</xref>(b). One observes that the statistical error of the fit including the logarithmic correction is smaller than that of the linear fit. An estimate of the anomalous diffusion constant from the logarithmic fit is shown in <xref ref-type="fig" rid="fig8">Figure 8</xref>(c). This means 1) numerical consistance with the mathe- matical result obtained by B&#225;lint and Gou&#235;zel [<xref ref-type="bibr" rid="scirp.58453-ref53">53</xref>] , 2) the Bunimovich stadium billiard, although considered a non-generic billiard in quantum chaos due to the “bouncing ball states” of wave solutions, is a very interesting system in classical chaos in the asymptotic regime of many bounces, which displays anomalous diffusion behaviour, 3) the length matrix <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x272.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x273.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x274.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x275.png" xlink:type="simple"/></inline-formula> which generates universal behaviour in <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x272.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x273.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x274.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x275.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x276.png" xlink:type="simple"/></inline-formula> and<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x272.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x273.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x274.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x275.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x276.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x277.png" xlink:type="simple"/></inline-formula>, contains infor- mation allowing to distinguish normal diffusion from anomalous diffusion.</p></sec><sec id="s7"><title>7. Summary</title><p>This paper is about classical chaos occuring widely in nature, for example in astrophysics, meteorology and dynamics of the atmosphere, fluid and ocean dynamics, climate change, chemical reactions, biology, physiology, neuroscience, or medicine. We have suggested to extend random matrix theory, used in chaotic quantum systems, to classically chaotic and integrable systems. We have studied fully chaotic as well as integrable billiards and used a statistical description based on the length of trajectories to discriminate chaotic versus inte- grable behaviour.</p><p>Results:</p><p>1) In chaotic billiards (stadium and Sinai billiards), the NNS distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x278.png" xlink:type="simple"/></inline-formula> as well as <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x278.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x279.png" xlink:type="simple"/></inline-formula> obtained from the length matrix show GOE behaviour, i.e., they are universal. The implication is that RMT not only models spectral statistical fluctuation properties in chaotic quantum systems, but goes beyond and applies as well to chaotic classical systems.</p><p>2) The distribution of length matrix elements <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x280.png" xlink:type="simple"/></inline-formula> itself is universal to leading order. The difference between the shape of <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x280.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x281.png" xlink:type="simple"/></inline-formula> and a Gaussian distribution gives a quantitative measure of how much the motion of the billiard ball in a chaotic billiard differs from a random walk.</p><p>3) For the integrable rectangular billiard we find a correlation coefficient<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x282.png" xlink:type="simple"/></inline-formula>, in spectral rigidity <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x282.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x283.png" xlink:type="simple"/></inline-formula> and in the NNS distribution <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x282.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x283.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x284.png" xlink:type="simple"/></inline-formula> strong evidence for rigid behaviour and strong correlation between neigh- bour eigenvalues. Such behaviour can be understood from the observation that integrability introduces strong correlations in length matrix elements. This proliferates to the spectra.</p><p>4) In contrast, for integrable quantum systems the NNS distribution generally shows Poissonian behaviour with correlation coefficient <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/25-7402611x285.png" xlink:type="simple"/></inline-formula> (there are a few exceptions). Thus from the point of view of level spacing distributions, a marked difference shows up between the classical and the quantum world. In our opinion, such difference is due to the group properties/symmetries of classical trajectories.</p><p>Future directions:</p><p>1) We plan to do numerical studies to investigate if universality also holds in chaotic potential systems.</p><p>2) We hope that our findings may contribute to obtain a unified description of both, quantum and classical chaos, and help understanding why quantum chaos is typically weaker than classical chaos, e.g., via an effective quantum action [<xref ref-type="bibr" rid="scirp.58453-ref55">55</xref>] [<xref ref-type="bibr" rid="scirp.58453-ref56">56</xref>] .</p><p>3) The global statistical approach to classical chaos proposed here may help to give insight into the problem of ergodicity breaking in Hamiltonian systems (e.g., dense packing of discs in the Lorentz gas model [<xref ref-type="bibr" rid="scirp.58453-ref27">27</xref>] ).</p></sec><sec id="s8"><title>Acknowledgements</title><p>We are thankful to Prof. L. J. Dub&#233; for insightful discussions on chaotic dynamics and to O. Blondeau-Fournier for discussions and his assistance in performing simulations and analyses presented in Section 3.1 and 4.1. H. Kr&#246;ger is grateful to Prof. Chernov for discussions on central limit theorems in chaotic billiards. This work has been supported by NSERC Canada.</p></sec><sec id="s9"><title>Cite this paper</title><p>Jean-Fran&#231;oisLaprise,AhmadHosseinizadeh,HelmutKr&#246;ger, (2015) Universality in Statistical Measures of Trajectories in Classical Billiard Systems. Applied Mathematics,06,1407-1425. doi: 10.4236/am.2015.68132</p></sec></body><back><ref-list><title>References</title><ref id="scirp.58453-ref1"><label>1</label><mixed-citation publication-type="other" xlink:type="simple">Mehta, M.L. (1960) On the Statistical Properties of Level Spacings in Nuclear Spectra. Nuclear Physics, 18, 395-419.  
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