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Sjöstrand, T., Ask, S., Christiansen, J.R., Corke, R., Desai, N., Ilten, P., et al. (2015) An Introduction to PYTHIA 8.2. Computer Physics Communications, 191, 159-177.
https://doi.org/10.1016/j.cpc.2015.01.024

has been cited by the following article:

• TITLE: Optimizing the Matrix Element Method with Deep Neural Networks for High-Energy Physics Analyses

  AUTHORS: Olokunboyo A. Olaiya

  KEYWORDS: Deep Neural Network, Matrix Element Method, Regressions

  JOURNAL NAME: Journal of High Energy Physics, Gravitation and Cosmology, Vol.11 No.3, July 7, 2025

  ABSTRACT: The Matrix Element Method (MEM) is a widely used algorithm in experimental and theoretical high-energy physics (HEP) analyses. The MEM is based on the Lagrangian method to assess the compatibility of experimental events with a hypothetical process. The matrix element is then regressed with the help of a Deep Neural Network (DNN). The integration results can be approximated with DNN, which makes it possible to use the MEM for parameter scans, innovative physics searches, and other uses. The DNN can be trained to nearly mimic the outcomes of the direct numerical integration of the matrix element. It is possible to identify the specific ways in which the parton shower impacts the analysis by using the method to analyze these occurrences using fixed-order calculations. The method can, in theory, be applied to any measurement; however, processes involving intermediate resonances and leading to many-particle final states are anticipated to yield the highest improvement compared to cut-based analysis techniques. This research provides insights to understand the importance of optimizing machine learning strategies and fine-tuning regressions while also exploring the use of symmetry in particle physics with an interest in particle interactions and decays.