[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122244-en":3,"doc-seo-122244-105":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":20,"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},122244,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","New Gravitational Wave Discoveries Enabled by Machine Learning","Gravitational-wave detection has transformed insight into cosmic dynamics, while machine learning aims to accelerate detection and parameter estimation as event rates rise. The study introduces the first machine-learning-enabled detections of new gravitational-wave candidate events from a network of interferometric detectors, using the ResNet-based AresGW deep-learning code. Enhancements include hierarchical trigger classification with improved noise and frequency filtering, reducing the false alarm rate and increasing event counts in the effective training mass ranges. Astrophysical significance is evaluated via a logarithmic ranking statistic and injections into O3 data, supported by time-domain spectrograms, parameter estimation, and reconstruction. Performance is further validated on multiple two-detector setups and O1/O2 observational data.","arXiv :2407 .07820v 1 [gr-qc] 10 Jul 2024  \nNew Gravitational Wave Discoveries Enabled by Machine Learning  \nAlexandra E. Koloniari, 1 Evdokia C. Koursoumpa, 1 Paraskevi Nousi,2 Paraskevas Lampropoulos, 1 Nikolaos Passalis,3 Anastasios Tefas,3 and Nikolaos Stergioulas 1  \n1 Department of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece  \n2 Swiss Data Science Center, ETH, Z¨urich, Switzerland  \n3 Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece (Dated: July 11, 2024)  \nThe detection of gravitational waves has revolutionized our understanding of the universe, offering unprecedented insights into its dynamics. A major goal of gravitational wave data analysis is to speed up the detection and parameter estimation process using machine learning techniques, in light of an anticipated surge in detected events that would render traditional methods impractical. Here, we present the first detections of new gravitational-wave candidate events in data from a network of interferometric detectors enabled by machine learning. We discuss several new enhancements of our ResNet-based deep learning code, AresGW, that increased its sensitivity, including a new hierarchical classification of triggers, based on different noise and frequency filters. The enhancements resulted in a significant reduction in the false alarm rate, allowing AresGW to surpass traditional pipelines in the number of detected events in its effective training range (single source masses between 7 and 50 solar masses and source chirp masses between 10 and 40 solar masses), when the new detections are included. We calculate the astrophysical significance of events detected with AresGW using a logarithmic ranking statistic and injections into O3 data. Furthermore, we present spectrograms, parameter estimation, and reconstruction in the time domain for our new candidate events and discuss the distribution of their properties. In addition, the AresGW code exhibited very good performance when tested across various two-detector setups and on observational data from the O1 and O2 observing periods. Our findings underscore the remarkable potential of AresGWas a fast and sensitive detection algorithm for gravitational-wave astronomy, paving the way for a larger number of future discoveries.  \nPACS numbers: 04.30.-w,95.30.Sf,95.85.Sz  \nI. INTRODUCTION  \nThe cosmos whispers its secrets through gravitational waves, subtle ripples in the fabric of spacetime that carry profound insights into the universe’s most enigmatic phenomena. In the quest to decipher these cosmic murmurs, the convergence of machine learning (ML) algorithms and gravitational wave astronomy has ushered in a new era of discovery, promising unprecedented sensitivity and efficiency in detecting these elusive signals.  \nDuring the initial three observing runs (O1-O3) conducted initially by the LIGO-Virgo Collaboration [1, 2], with the later addition of Kagra [3], around 90 gravitational wave (GW) events were confidently identified and published in the GWTC catalogs [4–7] . These events primarily consisted of binary black hole (BBH) mergers, alongside a minority of binary neutron star (BNS) and neutron star-black hole (NSBH) systems. Additional events were published in the OGC catalogs [8–11] and the IAS catalogs [12–14] and updated significance of events was discussed with the pycbc KDE pipeline in [15] . As we find ourselves in the middle of the fourth observing run (O4) and anticipate the dawn of upgraded or nextgeneration detectors, such as LIGO-India [16], Voyager [17], and Virgo nEXT, Cosmic Explorer [18], Einstein Telescope [19], and NEMO [20], the need for more efficient gravitational wave detection algorithms becomes crucial [21] . Traditional matched-filtering methods face  \ncomputational challenges for near-real-time processing, compounded by the complexities of accurately detecting systems with non-aligned spins. Unmodeled search techniques, while","cbCaieJ0hr4uoxhV","https://ap.wps.com/l/cbCaieJ0hr4uoxhV","pdf",13003883,1,30,"English","en",105,"# Introduction\n## Gravitational-wave detections and observational context (O1–O4)\n## Machine learning for gravitational-wave data analysis\n## MLGWSC-1 and prior AresGW results\n## Present improvements and evaluation on O3 data","[{\"question\":\"What is the main goal of using machine learning in gravitational-wave data analysis?\",\"answer\":\"Speed up detection and parameter estimation as the number of observed events increases, making traditional methods less practical for near-real-time needs.\"},{\"question\":\"What key enhancements were added to the AresGW deep-learning code?\",\"answer\":\"AresGW received improvements such as a new hierarchical classification of triggers based on different noise and frequency filters, increasing sensitivity and lowering the false alarm rate.\"},{\"question\":\"How are the new candidate events’ astrophysical significances assessed?\",\"answer\":\"The work uses a logarithmic ranking statistic and injection studies into O3 data, alongside time-domain spectrograms, parameter estimation, and waveform reconstruction.\"}]","New Gravitational Wave Discoveries Enabled by Machine Learning | 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is the main goal of using machine learning in gravitational-wave data analysis?","Question",{"text":75,"@type":76},"Speed up detection and parameter estimation as the number of observed events increases, making traditional methods less practical for near-real-time needs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What key enhancements were added to the AresGW deep-learning code?",{"text":80,"@type":76},"AresGW received improvements such as a new hierarchical classification of triggers based on different noise and frequency filters, increasing sensitivity and lowering the false alarm rate.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the new candidate events’ astrophysical significances assessed?",{"text":84,"@type":76},"The work uses a logarithmic ranking statistic and injection studies into O3 data, alongside time-domain spectrograms, parameter estimation, and waveform 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