The stages of signal detection and denoising belong to the very beginning of the pipeline of multi-messenger astronomy and lay the foundations for the reliability of all future analysis. Modern observatories constantly receive huge amounts of various types of data, which include not only actual astrophysical information but also instrument and environment noises, as well as statistical noise. With the increase in the sensitivity of detectors, the volume and diversity of observation data have also grown. In particular, Vera C. Rubin Observatory is forecasted to provide around 20 TB of images each night and about 10 million transients alerts each observing night, which cannot be checked manually [4][19]. Furthermore, state-of-the-art gravitational wave detector systems constantly collect strain data sampled at a high rate from geographically distributed interferometers, whereas neutrinos and cosmic rays detectors constantly measure the interaction of billions of particles of which only a small percentage come from astrophysical origins [4][12]. Therefore, the effective detection of signals and suppression of noises have become crucial for modern multi-messenger astronomy.

Prior to the extensive use of artificial intelligence, the task of detecting signals depended largely on well-defined and robust statistical algorithms for individual observatories. In gravitational-wave astronomy, matched filtering is the best method to detect the compact binary coalescence when there are precisely modeled waveforms, while Bayesian inference and coherent excess power search algorithms are utilized in the detection of weakly modeled and unmodeled transients [3][20]. But this method involves correlation with hundreds of thousands of waveform templates that leads to an exponential increase in computation as a result of sensitivity improvements and increasing parameter space covered. Similar issues exist in electromagnetic surveys where transient detections depend on image differencing and statistical filtering to reduce false detections, and in neutrino observatories where likelihood-based reconstruction methods are employed to distinguish rare astrophysical signals from the strong background of atmospheric neutrinos [12]-[14]. Even though these algorithms are essential in astronomical data analysis, they fail to meet the requirement of being fast enough for multi-messenger detection.

AI technology has thus been seen as an auxiliary technology that greatly enhances the speed of signal detection without compromising on the statistical methods. Instead of substituting conventional methods, AI methods have been incorporated into the current detection process pipeline as means of quickly screening potential signals and artifacts, and performing noise modeling prior to the use of more computationally intensive methods. Such an approach helps in significantly cutting down computation time while maintaining the statistical reliability of conventional detection methods [21].

One of the earliest successes in this domain was demonstrated by George and Huerta [14] who constructed deep convolutional neural networks to detect compact binary merger signals directly from the noisy strain data from the LIGO observatory. Such a technique was able to achieve sensitivity similar to conventional matched filtering techniques but reduced inference time from the template-bank searches to just a few milliseconds, making it possible to perform real-time gravitational wave detection. Later developments in this direction have included the use of deeper neural networks, transfer learning, and attention mechanisms to make such networks more robust against non-Gaussian detector noise and glitches that are experienced during observing campaigns [5][21]. Another important use case of AI in gravitational-wave astronomy is the suppression of noise. Noise in detectors is non-stationary in nature and also contains transient noise which is hard to model via traditional statistical methods alone. Methods such as deep denoising autoencoders, variational autoencoders, generative adversarial networks, and most recently diffusion models have been used to reconstruct astrophysical waveforms and retain their physical features [17][22]. These have achieved success in the recovery of gravitational waves at low signal-to-noise ratio and thus increasing the detectability of compact binary mergers and other transient phenomena. While the use of diffusion models in operation is in the initial stages, recent studies suggest a lot of promise in waveform reconstruction under difficult observation conditions.

Consequences of the use of signal detection through AI technology go beyond gravitational wave astronomy. In optical time domain surveys like ZTF and LSST, which are carried out by the Vera C. Rubin Observatory, there is continuous monitoring of the dynamic universe and millions of transient candidates are produced. Where ZTF generates several hundreds of thousands of transient candidates per night, the anticipated number for LSST will be about 10 million per night, the great majority of which do not represent true astrophysical transients but either image artifacts, moving objects in the Solar System, or varying foreground objects [4][23]. Machine-learning classifiers have thus become a must for “real-bogus” classification, classifying real astronomical events from false signals, with high precision, and significantly reducing the number of candidates that need human intervention [19][24]. Comparable trends are observed in high-energy astrophysics. Only a tiny number of events at neutrino detectors like IceCube are of astrophysical origin, while most other events arise from atmospheric muons and neutrinos [12]. For this reason, machine learning has become an essential part of event selection and background rejection, utilizing the distribution of events recorded by photomultiplier arrays [25]. The same applies to the Cherenkov Telescope Array (CTA), where deep learning methods are used for separating gamma rays and hadrons, identifying particles, and reconstructing energy.

Table 2. Representative AI applications for signal detection and denoising in multi-messenger astronomy.

Although considerable progress has been made, there are still a few hurdles that need to be overcome in order for AI-based detection methods to be implemented in future observatories. The effectiveness of these approaches is highly dependent on the data they are trained with, thus being vulnerable to detector upgrades, variations in the noise environment, and novel astrophysical phenomena. Besides, issues related to the interpretability of certain deep learning architectures also bring about concerns about statistical significance and physical validity of the results obtained using these models. This has sparked research interests in hybrid systems that would use physics-based detectors along with machine learning and physics-informed neural networks where the governing physical laws would be embedded directly into the training process [17][18]. Consequently, it can be stated that AI has made signal detection and noise reduction a computationally feasible task from what was a computationally very expensive process before. Through complementing existing statistical approaches to data analysis rather than substituting them, AI provides fast detection of various astrophysical signals from gravitational waves to cosmic rays, and in doing so provides an important starting point for further classification of those signals. Table 2 presents representative applications of artificial intelligence across major astronomical observatories and facilities. It highlights the relationship between the underlying astronomical data, the AI techniques employed, and their demonstrated outcomes, providing an overview of how AI is being integrated into diverse observational and data-analysis workflows.

Upon successful identification of a potential astrophysical signal, the next step within the multi-messenger pipeline is that of identifying the physical cause of the signal and determining the physical properties of the source responsible for the signal. Event classification serves the purpose of categorizing astrophysical events into different groups depending on the class of astrophysical phenomenon, such as binary black hole mergers, binary neutron star mergers, neutron star-black hole mergers, supernovae, tidal disruption events, active galactic nuclei, or high-energy neutrino sources. Following this step comes parameter estimation, where the intrinsic and extrinsic parameters of the source are determined, such as masses, spins, luminosity distances, inclination angles, sky locations, and any other relevant parameters needed to interpret the scientific significance of the astrophysical phenomenon. Historically, source characterization has been done using Bayesian inference and stochastic sampling methods such as Markov Chain Monte Carlo (MCMC) and Nested Sampling to obtain a statistically sound posterior distribution of the parameters of the sources. Such approaches are still considered state-of-the-art in gravitational wave parameter estimation due to their ability to provide an uncertainty quantification. Nevertheless, as dimensionality of the parameter space increases, the cost of Bayesian inference becomes prohibitive. The analysis of compact binary merger can take hours or even days to converge, thus preventing fast distribution of the information about the sources. Another area that promises significant speedup of the process is artificial intelligence. Instead of substituting Bayesian inference with machine learning approaches, it is now commonly used as an auxiliary tool providing fast initial estimates for further Bayesian inference [2][5].

A groundbreaking experiment was conducted by George and Huerta, who presented the Deep Filtering model using deep convolutional neural networks to perform end-to-end processing of gravitational waves signals. The Deep Filtering method performed signal classification and source parameter estimation simultaneously and directly from noisy detector signals, obtaining detection results with the level of accuracy similar to matched filtering but performing inference much faster than traditional methods—in the range of milliseconds instead of hours. The study showed the potential of deep learning for estimating component masses of binary black holes directly from raw time-series data, enabling real-time follow-up observations in multi-messenger astronomy [5][14]. Recent research in the field has developed even more efficient methods for AI-assisted parameter estimation using probabilistic machine-learning algorithms capable of producing Bayesian posteriors. Several machine-learning algorithms including deep neural networks, normalizing flows, Bayesian neural networks, and others were tested and shown to produce accurate parameter estimation results, substantially reducing computational cost compared with conventional Bayesian analysis and enabling low-latency inference. This is especially useful during gravitational wave observing runs since low-latency inference increases chances of finding counterparts of the source.

Besides being used for gravitational-wave astronomy, AI finds applications in modern optical time-domain surveys such as the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). The latter, for example, is anticipated to produce up to 10 million alerts per night. Fully automatic classification methods must be employed for separating transient sources of interest from variable stars, moving Solar System objects, and instrument noise. Therefore, machine-learning is implemented into the pipeline of optical surveys for transient event classification and target prioritization for spectroscopic observations and multiwavelength follow-up [2][5]. Moreover, artificial intelligence has become an indispensable tool for high-energy astronomy as well. For instance, the IceCube Neutrino Observatory uses machine learning for classifying astrophysical neutrinos against an overwhelmingly large atmospheric background through making use of the information about spatial distribution, timing, and energy of events obtained by the detector. Machine learning is applied for similar purposes in gamma-ray astronomy as well, for improving the identification of particles, discrimination between gamma-ray and hadron events, and energy estimation in Imaging Atmospheric Cherenkov Telescopes.

However, several significant challenges continue to arise despite these advancements. The vast majority of the supervised learning techniques rely heavily on the quality of the training data sets used and may be unreliable in cases where the properties of detectors and populations of celestial objects differ from those used in training. Additionally, uncertainty quantification remains an open challenge since proper confidence intervals play a pivotal role in precision astrophysics. Hence, hybrid Bayesian-AI methods and physics-aware machine learning have been gaining popularity in recent years. All things considered, AI has enabled the transition of event classification and parameter estimation from time-consuming operations into fast and efficient parts of the multi-messenger astronomy workflow. As a result, the process of extracting scientifically valuable information about sources right after their discovery becomes very fast and contributes greatly to successful observational campaigns involving various types of signals [2][5]. Additionally, the claimed advantages of the AI techniques can be measured by numerical quantities, such as inference time, computational costs, classification accuracy, and parameter estimation accuracy. Specifically, in case of gravitational wave analysis, the deep learning algorithms have proven themselves able to achieve signal detection and parameter estimation much faster than Bayesian inference techniques and, therefore, are adequate to low-latency requirements. In turn, transient classification systems in optical astronomy are specifically tailored to work on very large-scale alert streams, whereas event selection AI techniques in high-energy observatories allow for efficient detection of astrophysical signals out of a substantial background. The conclusion one may make is that the main advantage provided by AI techniques is not just better classification, but rather fast provision of scientifically meaningful results within a computationally feasible timeframe. Table 3 summarizes representative AI-driven applications across major astronomical facilities, with emphasis on the specific computational tasks enabled by these approaches and their resulting scientific contributions. The examples demonstrate the role of AI in accelerating event characterization, improving parameter inference, prioritizing transient candidates, managing high-volume alerts, and enhancing the identification of astrophysical signals across different messenger channels.

Table 3. Representative AI applications for event classification and parameter estimation.

The capability to study one and the same astrophysical phenomenon using a number of messengers, such as gravitational waves, electromagnetic radiation, neutrinos, and cosmic rays is among the major achievements in modern-day astrophysics. Given that each of the messengers studies distinct physical phenomena and locations in the source, a joint study of all the data offers much better insight into the studied phenomenon compared to a single observation. Thus, the success of the multi-messenger approach in astrophysics not only relies upon detecting individual events but also upon fast and accurate correlation of events observed by different geographically separated facilities. The power of multi-messenger studies has been exemplified in the case of the neutron star binary merger GW170817, which has been observed via gravitational waves using the LIGO-Virgo detector array, followed by the association with a short gamma-ray burst, GRB 170817A and optical kilonova from more than seventy observatories around the world. This set of data has opened up new windows to study neutron star mergers, r-process nucleosynthesis, jet formation, and measurement of the Hubble constant [1][2]. Similarly, the association of the high-energy neutrino event IceCube-170922A with the flaring blazar TXS 0506+056 provided strong evidence for an astrophysical source of high-energy neutrinos, supported by the temporal and spatial coincidence and subsequent multi-wavelength observations [3].

Conventionally, the cross-messenger associations have been made via the coincidence analysis using temporal, spatial, and astrophysical consistencies. Events recorded in different observatories are analyzed in predefined time intervals and localizations, followed by likelihood or Bayesian statistics to calculate the probability of their origin from a common source of astrophysical nature. While this approach is statistically sound, its computational cost grows fast along with the increasing number of observatories and transient alerts. For example, the Vera C. Rubin Observatory is expected to generate approximately 10 million transient alerts per night, while future third-generation gravitational-wave observatories are expected to substantially increase the number of detectable compact-binary events [10][12]. The use of artificial intelligence, however, has proven to be a powerful approach for the integration of heterogeneous datasets in astronomy. While previous coincidence methods analyzed individual messengers and correlated them statistically, AI is able to analyze the temporal evolution, spectral properties, localization errors, morphologies of sources and astrophysical context all at once. Machine-learning methods allow the detection of correlations in nonlinear form and in consequence, the detection of associations that would not have been detected by using classical techniques [6].

The recent development in the multimodal deep learning domain has opened up further possibilities in this direction. State-of-the-art neural network architectures are able to fuse heterogeneous inputs such as gravitational wave time series, optical images, spectra, neutrinos, and catalog data into one latent representation which can be used for source characterization. For instance, Graph Neural Networks (GNN) is an area that has received considerable interest due to the natural structure of graphs formed by astronomical observations relating to transient events, host galaxies, observatories and their follow-ups [7]. Another field where artificial intelligence has played an important role is the operation of the current astronomical alert brokers. Facilities like ANTARES, Lasair, ALeRCE, and Fink play a critical role in the modern system of transient discovery. These intelligent brokers continuously consume massive alert streams produced by wide-field surveys, cross match the detections against archive catalogues, obtain contextual information, use automatic machine learning to classify the alerts, and flag the scientifically interesting targets for follow-up observations. For instance, the ALeRCE broker uses real-time stamp classification combined with light curve classification to identify astrophysical transients among instrument artifacts, whereas ANTARES and Fink make use of scalable machine learning pipelines that can process millions of alerts at once with low latency [8]-[11]. Intelligent alert brokers would become an essential component during the era of the Rubin Observatory when the massive amount of alerts would need automation in decision making.

Artificial intelligence for cross-messenger astronomy is now moving forward from task-specific neural networks to multimodal foundation models that can learn joint representations across different types of astronomical data. Drawing motivation from recent developments in vision-language models and large language models, such architectures enable integration of images, spectra, light curves, time series, metadata, and even natural-language descriptions into a single embedding space. The use of such models enables conducting multiple tasks of interest such as event association, source classification, anomaly detection, parameter estimation, and follow-up scheduling without the need for separate models for each one of them. Initial research shows that transformer-based models and self-supervised pretraining can be efficiently used in order to take advantage of huge amounts of unlabeled astronomical data provided by future instruments. Combined with intelligent alerting brokers and robotic telescope systems, such technologies will enable increasingly autonomous observatories capable of conducting observations, choosing follow-up strategy, and making scientific discoveries in a fully automated way [26]-[29].

Nevertheless, there are a number of obstacles to overcome. Multimessenger events differ from each other in temporal cadence, spatial resolution, localization, sensitivity of detectors, and quality of measurements. In addition to this, the majority of astrophysical transients are inherently rare objects, which means a lack of labeled data for training supervised machine learning algorithms. Thus, domain adaptation, transfer learning, self-supervised learning, and federated learning can be considered promising directions for generalization of models and collaboration between geographically dispersed observatories without data exchange [6][12]. The problem of interpretability of the results of machine learning and their statistical significance is another important problem to consider. Follow-up observations imply high costs and quick decisions. This is why astronomers need not only precise classification of events, but also reliable estimation of confidence levels. Therefore, increasing attention is paid to hybrid approaches based on machine learning and Bayesian inference, probabilistic graphical models, and explainable AI [6][13]. Overall, AI has revolutionized the process of associating events across multiple messengers from a manual and statistically intensive process to an intelligent, efficient, and low-latency part of the multi-messenger astronomical pipeline. By combining different observations, ranking candidate events, and allowing fast dissemination of scientifically important alerts, AI provides a great deal of added value to joint observing campaigns using next-generation telescopes.

It is critical to draw a line between AI applications that have already been implemented in actual or almost actual astronomical pipelines and those which are still on the research and prototype stage. Among more developed AI applications are machine learning-based alert classification, transient filtering and prioritization, with multiple astronomical alert brokers showing their work on large datasets. On the other hand, multimodal foundation models, cross-messenger representation learning, and fully autonomous follow-up systems are still developing areas of research, which need further validation, quantification of uncertainties and robustness testing before actual implementation and integration with heterogeneous observatory infrastructure. For this reason, the applications presented in this review are discussed both in terms of their actual capabilities and future perspectives of multi-messenger astronomy. Table 4 provides examples of platforms and techniques that use artificial intelligence to carry out multi-messenger event correlation, alert filtering, and heterogeneous data fusion. These examples demonstrate how artificial intelligence can be utilized to facilitate the search for multi-messenger counterparts, real-time filtering and ranking of astronomical alerts, and the fusing of observations from various telescopes and messengers.

Table 4. Representative AI applications for cross-messenger event association and data fusion.

After astrophysical transients have been detected, classified, and connected through multiple messengers, the crucial task within the process of multi-messenger astronomy is the localization of the sources in the sky. Correct localization allows for prompt follow-up observations using electromagnetic waves, host-galaxy identification, and analysis of the surroundings of the sources. As many astrophysical transients change on timescales from seconds to days, localization has to be both fast and precise. Any delay in creating sky maps may lead to missing of the fading transient objects, thus, limiting the scientific output of multi-messenger observations [1][3][35]. The necessity for fast localization was made clear during the observation of the binary neutron star merger event called GW170817, during which the first detection of the gravitational waves was followed by a coordinated search using more than seventy different observatories on the ground and in space. The rapid gravitational-wave detection was followed by a coordinated electromagnetic search over a relatively large sky region, ultimately leading to the identification of the optical counterpart and its host galaxy NGC 4993 [1][3][35][36].

The conventional localization techniques vary greatly between different messengers. In gravitational wave astronomy, the technique of localization consists of triangulation, in which the differences in arrival time, amplitude, and phase of the signal observed at different locations are used together with Bayesian inference to create a probability sky map. Fast localization pipelines such as BAYESTAR can rapidly generate probability sky maps following gravitational-wave detections, whereas more computationally intensive Bayesian parameter-estimation methods provide more detailed localization and source-parameter estimates at substantially higher computational cost [37]-[40]. Localization procedures differ for other types of messengers as well. Localization of high-energy neutrinos, for example, is done using the direction of trajectories of secondary particles created in the result of neutrino interactions within the detector volume in case of Ice Cube. The localization of sources of gamma-ray events is based on shower reconstruction, whereas the localization of optical transients is achieved via astrometric calibration and image differencing with sub-arcsecond accuracy. Despite the fact that each of the listed procedures turned out to be quite efficient independently, the fusion of localization data from various observatories is a computationally difficult task due to their heterogeneity [3][13].

An important issue is posed by the high localization uncertainties often observed in gravitational-wave observations. During the early Advanced LIGO observing runs, the limited detector network often resulted in large gravitational-wave localization regions, increasing the observational resources required for electromagnetic follow-up. The addition of further detectors such as Advanced Virgo and later KAGRA has helped reduce these uncertainties substantially through better triangulation geometry. However, next-generation detectors will still produce localization uncertainties large enough to make intelligent selection of follow-up observations a necessity [37][41]. AI has proved to be a suitable way out of these computational problems in that it learns the non-linear mapping between detector responses and source location directly from the observational data. Deep learning algorithms do not make use of the usual parameter estimation techniques of Bayesian inference since they do not involve repetitive sampling of high dimensional parameter spaces using computationally intensive likelihoods but rather achieve localization via one inference process after the training is completed.

Advancements in recent times have shown how useful Convolutional Neural Networks (CNNs), Temporal Convolutional Networks (TCNs), recurrent neural networks, and transformers are for gravitational-wave localization. The aforementioned models are capable of analysing strain data from detectors and learning the spatial relationships between different detectors without any need for explicit waveform template matching. This means that localization posteriors can now be created in milliseconds to seconds, which is orders-of-magnitude faster than conventional Bayesian sampling techniques [5][21][42]. The other significant advance in this area has been the use of normalizing flows and other methods of simulation-based inference for fast gravitational wave parameter estimation. Unlike traditional approaches that predict only a single sky location of a source, such methods learn the full posterior distribution of source parameters, comprising sky location, luminosity distance, inclination angle, and mass of a binary. This allows one to obtain uncertainty estimates similar to those obtained using Bayesian inference but at significantly lower computational costs [43].

AI technology is also being applied to localization beyond gravitational wave astronomy. Deep-learning systems are being used to perform image segmentation and centroid detection, as well as host galaxy classification, in optical sky surveys. Machine-learning techniques are used to aid beam forming and source extraction, as well as FRB localization, in radio astronomy, while high energy experiments are using AI methods for event reconstruction and direction estimation through detector response data. With the convergence of these trends in a number of observational fields, a unified localization framework is now emerging, where observations from different messengers can be brought together in a consistent probabilistic framework, greatly enhancing localization accuracy [3][44]. Another exciting approach is the use of Graph Neural Networks (GNNs) for localization tasks that include distributed detector networks. As the observatories themselves can be thought of as spatially connected networks, such an approach will help to easily represent the connections between the detector locations, arrival times, localization errors, and the observations made. This way of representing relations will help in fast propagation of information within the network of detectors and also help to efficiently combine heterogenous localization data obtained through gravitational-wave detectors, neutrino detectors, optical telescopes, and gamma-ray observatories. The workflow involved in AI assisted source localization is briefly described in Figure 4. Observations coming from various messengers are combined through the use of advanced artificial intelligence techniques to create probabilistic sky maps for source localization and verification purposes.

Figure 4. Schematic illustration of the AI-driven source localization pipeline in multi-messenger astronomy, from multi-observatory data acquisition to probabilistic sky mapping, follow-up prioritization, and source confirmation.

In summary, AI-based localization is one significant achievement of the multi-messenger astronomy pipeline in terms of computational speed and highly accurate localization. By employing deep learning, probabilistic inference, and graph-based data integration, AI technology achieves localization with great speed and improves collaboration among various observatories for their observations. Such ability becomes increasingly important because the number of transient events detected is growing due to new generations of telescopes being developed. After the rapid creation of source localization maps, the efficacy of AI-based source localization methods can be measured in terms of the following three major performance metrics: localization accuracy, computational latency, and scalability. Traditionally used Bayesian inference methods deliver highly accurate posterior probability distributions but suffer from high computational requirements since one needs to search through the whole parameter space by evaluating the likelihood multiple times. On the other hand, after training, deep learning models perform localization with only one step of forward inference, thereby saving multiple orders of magnitude in computational latency while delivering a similarly good performance of localization in a variety of astrophysical transient types. Recent investigations have shown that the use of AI-based localization systems enables the creation of probabilistic sky maps in milliseconds to seconds in comparison with minutes to hours that are required by traditional Bayesian parameter inference methods. Such achievements are especially useful for studying rapidly varying transients such as short gamma-ray bursts, kilonovae, and fast radio bursts, which offer valuable information about astrophysical processes at work [5][14][45][46].

In addition to the speed of computation, AI systems offer advantages with regards to scheduling the use of telescopes and the efficient use of observational resources. Rather than surveying all of the fields within the localization region equally, AI algorithms sort candidate sky fields according to their probabilities of localization, based on galaxy catalogues, observability requirements, weather, and availability of telescopes. Such intelligent scheduling substantially reduces unnecessary observations and raises the chances of finding the actual astrophysical counterpart in the early stages of follow-ups. Similarly, robotized telescope networks are now using such intelligent optimizations to coordinate their work automatically [37][47].

Nevertheless, AI-powered localization still faces several issues that need to be considered. First, the performance of supervised learning algorithms is highly dependent on the quality and representation of training data, whereas many astrophysical transients are rather rare in reality. As a result, simulated data sets play a key role in training algorithms and, thus, can lead to the problem of domain shift difference between simulation space and detector observations. Second, uncertainties associated with localization due to detector calibration problems, non-Gaussian instrumental noise, incomplete detector networks, and observational biases cannot be properly taken into account in deterministic deep-learning models. As a result, further improvement of uncertainty quantification methods is a promising field of research, with Bayesian deep learning, ensemble learning, simulation-based inference, and probabilistic neural networks among the most popular methods [44][48].

Further, another crucial aspect in this regard relates to the interpretability and robustness of AI-generated predictions. Given that telescope scheduling is often connected with high costs and time limitations, astronomers need localization systems which are able to present transparency through the estimation of confidence as well as physical uncertainty regions. Therefore, explainable AI approaches, hybrid Bayesian-machine learning techniques and physics-informed neural networks attract growing interest as ways to enhance the reliability and interpretability of the proposed AI-assisted localization systems. It is believed that such an approach will help improve confidence in automated decision-making systems while maintaining rigorous statistical background typical for astronomy [48][49]. The upcoming next-generation telescopes will emphasize the importance of intelligent localization algorithms even more. It is expected that both Einstein Telescope and Cosmic Explorer will produce significantly larger number of gravitational wave events with better localization precision, while the Vera C. Rubin Observatory, Square Kilometre Array, Cherenkov Telescope Array Observatory, IceCube-Gen2 and future space missions will produce unprecedented amounts of heterogeneous data. The integration of these sources of data into a single AI system will become possible by implementing increasingly advanced AI architectures that can handle various messengers, perform probabilistic source localization, coordinate telescope scheduling and update localization maps continuously [37][50].

Table 5. Representative AI techniques for source localization in multi-messenger astronomy.

Moving forward, the fusion of multimodal deep learning, graph neural network, transformers, simulation-based inference, and foundation models is anticipated to revolutionize source localization by making it a self-sufficient scientific exercise. In addition to being localized through a single algorithmic approach, future AI will work as intelligent frameworks that can integrate heterogenous data, determine uncertainty, make follow-up observations, and assist astronomers throughout the entire multi-messenger discovery process. Such advancements are an essential step towards fully automated telescopes that can respond to transients with little human involvement but maximum scientific benefit of multi-messenger astronomy. Table 5 shows the most prominent AI methods used for source localization, signal analysis, parameter estimation, and multi-messenger data fusion. The table describes the main features and potential uses of the most important deep learning and probabilistic algorithms and illustrates how the use of these methods can increase the efficiency and precision of multi-messenger events detection.

The discovery of gravitational waves made by the LIGO Scientific Collaboration in 2015 not only launched the field of gravitational-wave astronomy but also highlighted the significance of advanced computational techniques in processing weak signals present in detector noise [3]. Standard searches are based on the matched filtering approach where the data are correlated with a large set of waveform templates that represent different configurations of compact binary systems. The use of this technique is optimal when it comes to modelling of the signals but its computational complexity grows rapidly with increasing number of templates in the template bank especially for analyses involving low latency searches required for multi-messenger follow-ups [40]. However, artificial intelligence proved to be an efficient alternative since it learns the correspondence between the data from the detectors and the signals of astrophysical origin from training datasets. One of the first demonstrations of this approach was Deep Filtering where convolutional neural networks (CNNs) were used for detecting signals produced during the merger of binary black holes and estimating the properties of the sources in real time. After training, the neural network infers the results in a single forward pass thus dramatically reducing computation times down to milliseconds with the same sensitivity of detection compared to standard matched-filter searches at different signal-to-noise ratios [14][21].

Moreover, AI has considerably helped characterize detectors. There are many instrumental and environmental perturbations, known as glitches, affecting gravitational-wave interferometers, which could simulate real astrophysical signals and decrease the sensitivity of the searches. Gravity Spy uses a combination of citizen-science classifications and deep-learning image recognition algorithms to classify such transient noise artifacts. Through the classification of glitches from real gravitational-wave signals, this approach contributes to the improvement of detector performance and accuracy of gravitational-wave detection pipelines [52]. In recent years, machine-learning algorithms have also been utilized for quick parameter estimation and localization, resulting in nearly instantaneous generation of probabilistic sky maps used for follow-up observations [37][38]. As a consequence of all the advancements discussed above, AI has become an indispensable tool for analyzing data within the LIGO-Virgo-KAGRA framework. Rather than substituting Bayesian inference, machine-learning models have been employed in conjunction with conventional methods, providing rapid first-stage analyses to be further refined by time-consuming algorithms of parameter estimation. This sort of strategy is predicted to grow in importance due to an increased number of events detected at next-generation observatories [38][50].

State-of-the-art optical surveys of the sky are constantly observing the evolving Universe and detecting supernovae, variable stars, tidal disruption events, active galactic nuclei, and other transient phenomena. The growing sensitivity and field of view of wide-field telescopes resulted in the exponential increase of data volume, and the visual screening of transient candidates became impossible. For instance, the Zwicky Transient Facility provides hundreds of thousands of alerts each night, and the Vera C. Rubin Observatory is expected to produce almost 10 million alerts each night when conducting full-scale science operations [4][53]. Thus, the effective usage of this wealth of data requires automated analysis pipelines based on AI techniques. Modern machine-learning techniques are often used to classify transients using the photometric light curves, the morphology of images, the evolution of colors and the additional data from the astronomical catalogues. Different supervised learning techniques such as random forests, convolutional, recurrent neural networks, and transformer models show extremely good results in recognizing supernovae, variable stars, active galactic nuclei and instrument artifacts. This approach significantly decreases the number of false positives and allows to quickly identifying interesting events that require further spectroscopic study and observations at multiple wavelengths [32][54][55].

The other equally important advancement in recent years has been the development of broker systems that use artificial intelligence algorithms such as ALeRCE, Fink, and ANTARES. Such platforms are capable of ingesting alert streams, cross-matching new discoveries against existing catalogues, extracting contextual information, classifying objects using machine-learning, and delivering priority alerts to the astronomy community minutes after discovery. They allow astronomers to allocate their observational facilities on promising transients while keeping human interventions to a minimum [30][33][56]. ALeRCE, for instance, uses a combination of imaging classification and light curves to differentiate between astrophysical transients and instrumental artefacts while Fink uses scalable machine learning pipelines in conjunction with distributed computing to efficiently process Rubin-scale alert streams. Apart from classification, AI is helping with photometric redshift estimation, host galaxy identification, image segmentation, and anomaly detection. Through deep-learning algorithms, rare or novel types of transient events are being identified by recognizing unusual patterns that cannot be classified by rule-based classification schemes. As a result, it is expected that artificial intelligence will be playing an increasing role in maximising science from future time-domain surveys where data rates will be too large to be handled through conventional means of analysis [4][55].

Neutrino astronomy at high energies offers a special tool to study one of the highest energy regions in the universe, such as active galaxies, gamma-ray bursts, and core collapse supernovae. Neutrinos have a very low probability of interaction both with the matter and magnetic fields, making them ideal probes of the distant universe, where they can carry directional information about the origin of the neutrino events. Nevertheless, due to the weak cross-sections, their detection is quite difficult and requires kilometre-scale detectors like IceCube that can register a tiny amount of astrophysical neutrinos against the huge background of atmospheric muons and neutrinos [13]. The use of artificial intelligence in neutrino reconstructions helped to learn the complicated dependence between the signal and particle parameters and resulted in an increasing number of deep neural networks used to estimate the direction, energy, and topology of neutrino events from the recorded Cherenkov light images. As compared to conventional likelihood-based methods, machine learning algorithms not only allow for faster inference but also offer better performance in terms of angular resolution and energy estimations for different event categories [57][58]. It allows for more robust identification of neutrino signals from astrophysical sources.

Machine learning methods have also advanced the field of background rejection in order to separate true neutrino interactions from the background noise and atmospheric events. In recent years, efforts have been made to use graph neural networks and geometric deep learning to take advantage of the non-uniform geometry of large neutrino detector setups, thus making it possible to extract spatial and temporal data from photomultiplier data. This is becoming increasingly relevant for future projects like IceCube-Gen2, where the amount of volume and data increases greatly [59][60].

The scientific significance of AI within the field of multi-messenger astronomy may best be illustrated by separating out landmark discoveries from the AI methods that were applied during their follow-up and analysis. The detection of the electromagnetic counterparts of GW170817 was a discovery made possible by the initial detection of the gravitational waves by the LIGO-Virgo Collaboration, and the subsequent detection of GRB 170817A and AT2017gfo in follow-up observations. This was not an achievement of the AI; rather, AI algorithms have aided in the detection, classification, and follow-up of other such signals. Likewise, the association of IceCube-170922A with the flaring blazar TXS 0506+056 was based on the detection of neutrinos, their association, and multi-messenger observations. Although AI algorithms are a significant part of modern neutrino analysis, they should not be regarded as the cause of the historic discovery of multi-messenger associations [1][36][61][62].

The association of IceCube-170922A with the flaring blazar TXS 0506+056 provided strong evidence linking high-energy neutrino emission to an active galactic nucleus, although the interpretation is based on statistical association and should not be regarded as definitive proof from a single event. Event reconstruction with machine learning algorithms, quick-latency alerts, and multi-wavelength observations with gamma-ray, X-ray, optical, and radio telescopes allowed for thorough examination of the source and established strong links between the activity of active galactic nuclei and high energy neutrino emission [12][63]. This research stressed the necessity of intelligent selection and follow-ups for efficient scientific results from transients. In the future, with the emergence of new observatories including Einstein Telescope, Cosmic Explorer, Vera C. Rubin Observatory, Square Kilometre Array, Cherenkov Telescope Array Observatory, and IceCube-Gen2, the amount and complexity of observation data will drastically grow. That is why artificial intelligence will play a key role in creation of autonomous observatories that will be able to perform real-time detection, localization, intelligent scheduling and coordination of multi-messenger observations with minimum human participation [4][6][50][60]. In summary, these examples of representative applications show that artificial intelligence has progressed from being just a computing tool to being a foundational science infrastructure for astronomy in the modern world. The fast processing of different kinds of data, increased efficiency of observations, and the ability to make discoveries using various types of messengers are changing the nature of astronomical observations through the integration of AI into the observation process. The importance of the use of AI in future astronomical facilities will be crucial in the years to come. Even though there have been various forms of AI that have been designed to address particular issues in astronomy, most of them are similar and are used together in multi-messenger data analysis. Figure 5 below shows how different forms of AI are connected in relation to their representative applications in science. Table 6 shows the main areas of applications of AI in multi-messenger astronomy by listing the AI approaches used, their main scientific contributions, and the facilities where the approaches are being used. The table gives an overview of how AI is aiding gravitational wave detection, time domain optical surveying, neutrino event reconstruction, and multi-messenger observations.

Figure 5. Major artificial intelligence models and their representative applications in multi-messenger astronomy, together with the key technological enablers driving AI-assisted astronomical discovery.

Table 6. Representative applications of AI in multi-messenger astronomy, their primary scientific contributions, and the associated observational facilities and missions.

Supervised machine learning techniques require huge amounts of training data labeled correctly. However, in multi-messenger astronomy, validated astrophysical events are scarce, especially in the cases of gravitational waves, high-energy neutrinos, fast radio bursts, and electromagnetic counterparts of transient phenomena. Despite huge volumes of observational data that are produced every day by modern observatories, only a relatively small part of it is associated with well-defined sources that can be used for supervised learning. Such an unevenness hinders generalizability of deep-learning architectures and poses risks of overfitting to particular observational regimes [64]-[66].

One of the possible solutions to this problem is the use of simulated data sets obtained with physically based numerical simulations. Simulations allow for obtaining huge samples of training data, but they may not reflect the complete diversity of detector noise, instrument systematic effects, and even unknown astrophysical objects. As a result, machine learning algorithms trained on simulations tend to demonstrate subpar performance when dealing with observational data. Self-supervised learning, semi-supervised learning, active learning, and transfer learning offer alternative methods which reduce dependence on manually labeled data and allow the AI to make use of huge amounts of unlabeled astronomical data [28][67]-[69].

In many cases, current advanced AI methods are sophisticated nonlinear systems, whose reasoning processes are hard to explain. Despite the high predictive power of deep neural networks, understanding of the process underlying a certain prediction is challenging. In astronomy, where results of observations can affect theoretical models and require expensive follow-up observations, confidence in the science necessitates not only the prediction accuracy but also transparent reasoning and estimation of the uncertainties [70][71]. In recent times, a new focus in the research is put on the explainable artificial intelligence (XAI), which allows finding the observational features contributing to specific classification or parameter determination. Feature attribution, saliency maps, SHAP values, and attention visualization are some of the techniques providing useful insights into the model behavior and helping to confirm that AI systems use physically relevant features, not the artifacts of observations [17]. Another equally important aspect is the uncertainty estimation by means of Bayesian neural networks, Monte Carlo dropout, deep ensembles, and probabilistic machine-learning algorithms. The latter gives an opportunity to assess the confidence level of every prediction and make better decisions concerning the observation priorities and candidate’s selection.

Datasets in astronomy are inherently heterogeneous as data come from different telescopes with various wavelengths, sensitivities, spatial resolutions, and noise properties. Models trained on data from one telescope often perform worse when used on data from another instrument due to different detector behavior, different observation strategies, or other reasons. This issue, known as domain shift, is the main obstacle for generalized AI deployment in various astronomical surveys [64][72]. One way of solving domain shift problems is to transfer knowledge between different observational domains without having to train the model on a new dataset. Transfer learning, adversarial domain adaptation, and representation learning have already shown great results allowing models trained on some survey to be generalized to observations taken with other telescopes [68][72]. These techniques will be even more useful as observations from the Vera C. Rubin Observatory, Einstein Telescope, Square Kilometre Array, and IceCube-Gen2 start being used in future multi-messenger networks [4][59]. Instrument-independent AI models that can perform well on heterogenous data are one of the biggest challenges for the next decade.

The enormous data rates expected to be produced by the next-generation telescopes impose serious computational burdens on traditional approaches to data analysis and even contemporary AI approaches. In order to train large deep-learning models, one needs powerful computational resources, including fast GPUs, distributed computation systems, and effective storage solutions. Moreover, operational pipelines often need real-time inference with latency restrictions, especially for transients which need to be observed immediately [4][59][64]. Recent developments in cloud computing, distributed learning, specialized AI hardware, and high-performance computing made large-scale applications of AI in astronomy much more feasible. Techniques such as model compression, knowledge distillation, quantization, and effective parallel computation also provide a promising way to make AI solutions less computationally expensive while preserving predictive performance. Collaboration between astronomers, computer scientists, and engineers is needed to develop effective AI infrastructures to process data rates from future astronomical facilities [4][59][65].

Perhaps one of the most difficult obstacles to overcome in AI-based astronomy is building trust that such discoveries will be scientifically valid and not just statistically correct. In some cases, machine learning algorithms may use some underlying biases, instrument artifacts, or spurious correlations that are physically meaningless. Such methods can result in either false positives or false scientific conclusions based on predictions that have not been independently verified [64][70]. The next generation of astronomical AI tools should include physical considerations right from the beginning. Physics-informed neural networks, physics-bayesian and physics-machine learning models, and physics foundation models incorporating both observational data and physics principles are some promising trends in increasing reliability and interpretability [17]. No less crucial is developing standardized test datasets, reproducible benchmarks, and open-source software and methods for uncertainty estimation to allow for independent verification of AI models among different research groups [17][64][65]. It seems unlikely that AI will replace existing scientific approaches and become a replacement for theoretical and numerical modeling and observational data interpretation; instead, it will remain a scientific companion.

In summary, it will be through tackling the aforementioned challenges that will define the influence that artificial intelligence will have on the future of multi-messenger astronomy. Further advancements in explainable AI, uncertainty-driven learning, scaling computing power, domain adaptation, and physics-informed machine learning provide an outstanding chance of developing robust, interpretable, and autonomous scientific systems able to analyze the large amount of data generated by future observatories [17][64][65].