[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119983-en":3,"doc-seo-119983-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119983,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine Learning Applications in Gravitational Wave Astronomy","Gravitational wave astronomy has become a rapidly expanding observational field since the first detections in 2015, with the number of observed events expected to increase dramatically in the coming decades. As current detection and parameter-estimation algorithms become computationally impractical, machine-learning methods are used to accelerate both search and inference. The chapter surveys approaches including artificial neural networks and autoencoders for surrogate-model computation, deep residual networks for rapid high-sensitivity detections, and neural networks for building neutron-star models in alternative gravity theories.","arXiv :2401 .07406v 1 [gr-qc] 15 Jan 2024  \nMachine Learning Applications in Gravitational Wave Astronomy  \nNikolaos Stergioulas  \nAbstract Gravitational wave astronomy has emerged as a new branch of observational astronomy, since the first detection of gravitational waves in 2015 . The current number of 􀀤 (100) detections is expected to grow by several orders of magnitude over the next two decades. As a result, current computationally expensive detection algorithms will become impractical. A solution to this problem, which has been explored in the last years, is the application of machine-learning techniques to accelerate the detection and parameter estimation of gravitational wave sources. In this chapter, several different applications are summarized, including the application of artificial neural networks and autoenconders in accelerating the computation of surrogate models, deep residual networks in achieving rapid detections with high sensitivity, as well as artificial neural networks for accelerating the construction of neutron star models in an alternative theory of gravity.  \n1 Introduction  \nSince 2015, when the first gravitational waves (GWs) from a binary black hole (BBH) system were detected [1], GW detectionshave become increasingly common, moving closer to the point ofbeing a regular occurrence. After the third observing run (O3), the most recent catalog (GWTC-3, [2]) from the Advanced LIGO [3], Advanced Virgo [4] KAGRA [5, 6] collaboration contained 90 GW events, almost all of which were BBH mergers. The 4th observing run (O4) is currently underway and a larger number of BBH detections are expected [7] . The addition of a fifth interferometer, LIGO-India [8], is expected to significantly enhance both the sensitivity and the sky localization of the network. Moreover, third-generation ground-based detectors such as the Einstein Telescope [9, 10] and Cosmic Explorer [11, 12] are currently  \nNikolaos Stergioulas  \nDepartment of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece e-mail: [niksterg@auth.gr](niksterg@auth.gr)  \n2 Nikolaos Stergioulas  \nbeing developed and are anticipated to greatly expand our understanding of the astrophysical processes in the Universe [13, 14, 15] .  \nThe advances in GW astronomy described above were made possible by collaborative efforts in multiple areas. Accurate descriptions of the entire coalescence, including the full inspiral, merger, and ringdown, can be obtained in different ways, with IMRPhenomXPHM [16] and SEOBNRv5PHM [17] being two examples of waveform models. Recent implementations of these models take into account the spin-induced precession of the binary orbit and contributions from both the dominant and subdominant multipole moments of the emitted gravitational radiation. However, the increased complexity of the waveforms increases their computational cost.  \nAstronomical observations have enabled a number of attempts to determine the Equation of State (EoS) of Neutron Stars (NSs) . These include the NICER mass and radius measurements [18, 19, 20], the measurement of tidal deformability through gravitational waves [21, 22, 23, 24], as well as joint constraints, e.g.,[25, 26, 27, 28] . In particular, the detection of the binary NS merger GW170817 [29, 30] has prompted further research in this area.  \nIn recent years, there has been an increase in the utilization of machine learning approaches for the analysis of gravitational wave data (see [31, 32, 33] for reviews) . This chapter provides a summary of different machine learning applications to gravitational-wave astronomy presented in [34, 35, 36, 37] .  \n2 ANN-Accelerated Surrogate Models  \nSurrogate modeling has been provided to reduce the considerable computational cost of evaluating waveform models [38, 39], which can significantly speed up EOB waveforms (e.g. [38, 40, 41, 42, 43]) while still providing high accuracy within its valid parameter range. The SEOBNRv4 model has a three-dimensional","cbCaie8rpkSbnzHM","https://ap.wps.com/l/cbCaie8rpkSbnzHM","pdf",3237934,1,29,"English","en",105,"# Introduction\n## Observing runs and detector network\n## Waveform modeling and computational cost\n## Equation of state and neutron-star constraints\n## Motivations for machine learning\n# ANN-Accelerated Surrogate Models\n## Surrogate modeling for waveform evaluation\n## Neural residual modeling improvements","[{\"question\":\"Why are machine-learning methods needed in gravitational wave astronomy?\",\"answer\":\"The growing number of detections makes existing computationally expensive detection and parameter-estimation algorithms increasingly impractical.\"},{\"question\":\"How do surrogate models benefit gravitational-wave waveform computations?\",\"answer\":\"Surrogate modeling reduces the cost of evaluating waveform models while maintaining high accuracy within the model’s valid parameter range, enabling faster EOB waveform evaluation.\"},{\"question\":\"What role do artificial neural networks play in the surrogate-model approach described?\",\"answer\":\"Neural networks are used to estimate surrogate-model coefficients efficiently on CPU or GPU, and additional networks can model residual errors to improve mismatch performance.\"}]","Machine Learning Applications in Gravitational Wave Astronomy | 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are machine-learning methods needed in gravitational wave astronomy?","Question",{"text":75,"@type":76},"The growing number of detections makes existing computationally expensive detection and parameter-estimation algorithms increasingly impractical.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do surrogate models benefit gravitational-wave waveform computations?",{"text":80,"@type":76},"Surrogate modeling reduces the cost of evaluating waveform models while maintaining high accuracy within the model’s valid parameter range, enabling faster EOB waveform evaluation.",{"name":82,"@type":73,"acceptedAnswer":83},"What role do artificial neural networks play in the surrogate-model approach described?",{"text":84,"@type":76},"Neural networks are used to estimate surrogate-model coefficients efficiently on CPU or GPU, and additional networks can model residual errors to improve mismatch 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