[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122782-en":3,"doc-seo-122782-105":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},122782,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Applications of Machine Learning to Gravitational Waves - Dissertation","Gravitational waves provide a powerful view of the universe and its history, made possible by detectors operating worldwide since their first direct observation. Despite technological maturity, key challenges remain: standard search algorithms rely on assumptions that do not match contemporary detector behavior, and practical constraints such as high data rates and strict latency complicate real-time analysis. This dissertation introduces machine-learning-based methods to develop realistic search algorithms, validated through a mock data challenge, and demonstrated on the latter half of LIGO’s third observing run.","Applications of Machine Learning to Gravitational Waves  \nDISSERTATION  \nzur Erlangung des akademischen Grades doctor rerum naturalium (Dr. rer. nat.)  \nvorgelegt dem Rat der  \nPHYSIKALISCH-ASTRONOMISCHEN FAKULTÄT  \nder  \nFRIEDRICH-SCHILLER-UNIVERSITÄT JENA  \nvon  \nMgr. Ondřej Zelenka  \ngeboren am 14 . Februar 1995 in Most, Tschechische Republik  \nGutachter:  \n1. Prof. Dr. Bernd Brügmann  \nTheoretisch-Physikalisches Institut, Friedrich-Schiller-Universität Jena  \n2. Dr. hab. Michał Bejger  \nNicolaus Copernicus Astronomical Center of the Polish Academy of Sciences  \n3. Prof. Dr. Alicia Magdalena Sintes Olives  \nDepartament de Física & IAC3, Universitat de les Illes Balears  \nTag der Disputation: 2 . 11. 2023  \nFRIEDRICH-SCHILLER-UNIVERSITÄT JENA  \nAbstract  \nPHYSIKALISCH-ASTRONOMISCHE FAKULTÄT Theoretisch-Physikalisches Institut  \nzur Erlangung des akademischen Grades doctor rerum naturalium (Dr. rer. nat.)  \nApplications of Machine Learning to Gravitational Waves  \nby Mgr. Ondřej Zelenka  \nGravitational waves, predicted by Albert Einstein in 1916 and first directly observed in 2015, are a powerful window into the universe, and its past. Currently, multiple detectors around the globe are in operation. While the technology has matured to a point where detections are common, there are still unsolved problems. Traditional search algorithms are only optimal under assumptions which do not hold in contemporary detectors. In addition, high data rates and latency requirements can be challenging.  \nIn this thesis, we use new methods based on recent advancements in machine learning to tackle these issues. We develop search algorithms competitive with conventional methods in a realistic setting. In doing so, we cover a mock data challenge which we have organized, and which served as a framework to obtain some of these results. Finally, we demonstrate the power of our search algorithms by applying them to data from the second half of LIGO’s third observing run. We find that the events targeted by our searches are identified reliably.  \nFRIEDRICH-SCHILLER-UNIVERSITÄT JENA  \nZusammenfassung  \nPHYSIKALISCH-ASTRONOMISCHE FAKULTÄT Theoretisch-Physikalisches Institut  \nzur Erlangung des akademischen Grades doctor rerum naturalium (Dr. rer. nat.)  \nAnwendungen des maschinellen Lernens auf Gravitationswellen  \nvon Mgr. Ondřej Zelenka  \nGravitationswellen, die 1916 von Albert Einstein vorhergesagt und 2015 erstmals direkt beobachtet wurden, sind ein wichtiges Fenster zum Universum und seiner Vergangenheit. Derzeit sind mehrere Detektoren rund um den Globus in Betrieb. Auch wenn die Technologie inzwischen so weit ausgereift ist, dass Detektionen häufig vorkommen, gibt es immer noch ungelöste Probleme. Traditionelle Suchalgorithmen sind nur unter Annahmen optimal, die bei modernen Detektoren nicht zutreffen. Darüber hinauskönnen hohe Datenraten und Latenzzeiten eine Herausforderung darstellen.  \nIn dieser Arbeit verwenden wir neue Methoden, die auf den jüngsten Fortschritten im Bereich des maschinellen Lernens basieren, um diese Probleme anzugehen. Wirentwickeln Suchalgorithmen, die in einem realistischen Umfeld mit konventionellen Methoden konkurrieren können. Dabei behandeln wir eine von uns organisierte Mock Data Challenge, eine kompetitive Untersuchung verschiedener Methoden basierend auf realistischen, jedoch künstlich generierten Daten, welche als Rahmen zur Erlangung dieser Ergebnisse diente. Schließlich demonstrieren wir die Leistungsfähigkeitunserer Suchalgorithmen, indem wir sie auf die Daten der zweiten Hälfte des dritten LIGO-Beobachtungslaufs anwenden. Wir stellen fest, dass die Ereignisse, auf dieunsere Suchalgorithmen abzielen, zuverlässig wiedergefunden werden.  \nix  \nNotation and abbreviations  \nNew terms are first mentioned in italics, and new variables are defined using := . We make frequent use of the solar mass M⊙ =. 1.99·1030 kg. Inferred parameters are given as, e.g. , m 1 = 8 .211..46M⊙ , where the first value refers to the me","cbCaitauOPkWT0Tu","https://ap.wps.com/l/cbCaitauOPkWT0Tu","pdf",8107744,1,167,"English","en",105,"# Abstract\n## Motivation and challenges\n## Proposed machine-learning methods\n## Validation via mock data challenge\n## Application to LIGO O3","[{\"question\":\"Why do traditional gravitational-wave search algorithms underperform with current detectors?\",\"answer\":\"They are optimal only under assumptions that no longer hold for contemporary detector conditions, and operational constraints like high data rates and latency further hinder performance.\"},{\"question\":\"What does the dissertation contribute to gravitational-wave searches?\",\"answer\":\"It develops new search algorithms driven by recent advances in machine learning, aiming for competitiveness with conventional methods in realistic settings.\"},{\"question\":\"How are the proposed methods tested and validated?\",\"answer\":\"The work includes results derived from a mock data challenge it organized, and it demonstrates performance by applying the algorithms to data from the second half of LIGO’s third observing run.\"}]","Applications of Machine Learning to Gravitational Waves - 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