nam 22 uu 4500 dbt_mods_00059058 dbt_mods_00059058 ger DE-601 rda eng 530 sdnb Zelenka, Ondřej Author aut (DE-588)1308840888 Applications of machine learning to gravitational waves Zelenka, Ondřej Jena 2023 167 Seiten text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation, Friedrich-Schiller-Universität Jena, 2023 Gravitational 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. In 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. Gravitationswelle gnd Maschinelles Lernen gnd Convolutional Neural Network gnd Schwarzes Loch gnd thesis Brügmann, Bernd Reviewer rev (DE-588)12311571X Bejger, Michał Reviewer rev (DE-588)1309003645 Sintes, Alicia M. Reviewer rev (DE-588)1309004080 Friedrich-Schiller-Universität Jena Physikalisch-Astronomische Fakultät host institution his https://www.db-thueringen.de/receive/dbt_mods_00059058 object in context https://www.db-thueringen.de/servlets/MCRFileNodeServlet/dbt_derivate_00061839/Dissertation_OndrejZelenka.pdf raw object https://www.db-thueringen.de/rsc/thumbnail/dbt_mods_00059058.png preview http://uri.gbv.de/document/gvk:ppn:1869556445 uri