Analysis of Gravitational-Wave Signals from Binary Neutron Star Mergers Using Machine Learning

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dc.identifier.uri http://dx.doi.org/10.15488/7467
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/7520
dc.contributor.author Schäfer, Marlin Benedikt ger
dc.date.accessioned 2019-11-27T14:51:04Z
dc.date.available 2019-11-27T14:51:04Z
dc.date.issued 2019
dc.identifier.citation Schäfer, Marlin Benedikt: Analysis of Gravitational-Wave Signals from Binary Neutron Star Mergers Using Machine Learning. Hannover : Gottfried Wilhelm Leibniz Universität, Master Thesis, 2019, 100 S. DOI: http://dx.doi.org/10.15488/7467 ger
dc.description.abstract Gravitational waves are now observed routinely. Therefore, data analysis has to keep up with ever improving detectors. One relatively new tool to search for gravitational wave signals in detector data are machine learning algorithms that utilize deep neuralnnetworks. The first successful application was able to differentiate time series strain data that contains a gravitational wave from a binary black hole merger from data that consists purely of noise. This work expands the analysis to signals from binary neutron star mergers, where a rapid detection is most valuable, as electromagnetic counterparts might otherwise be missed or not observed for long enough. We showcase many different architecture, discuss what choices improved the sensitivity of our search and introduce a new multi-rate approach. We find that the final algorithm gives state of the art performance in comparison to other search pipelines that use deep neural networks. On the other hand we also conclude that our analysis is not yet able to achieve sensitivities that are on par with template based searches. We report our results at false alarm rates down to ~30 samples/month which has not been tested by other neural network algorithms. We hope to provide information about useful architectural choices and improve our algorithm in the future to achieve sensitivities and false alarm rates that rival matched filtering based approaches on. ger
dc.language.iso eng ger
dc.publisher Hannover : Gottfried Wilhelm Leibniz Universität
dc.rights CC BY 3.0 DE ger
dc.rights.uri http://creativecommons.org/licenses/by/3.0/de/ ger
dc.subject Gravitational waves eng
dc.subject Data analysis eng
dc.subject Machine Learning eng
dc.subject.ddc 530 | Physik ger
dc.title Analysis of Gravitational-Wave Signals from Binary Neutron Star Mergers Using Machine Learning eng
dc.type masterThesis ger
dc.type Text ger
dc.description.version publishedVersion ger
tib.accessRights frei zug�nglich ger


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