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

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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:

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Sum total of downloads: 247

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 sensitivitiesthat 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.
License of this version: CC BY 3.0 DE
Document Type: masterThesis
Publishing status: publishedVersion
Issue Date: 2019
Appears in Collections:Fakultät für Mathematik und Physik

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pos. country downloads
total perc.
1 image of flag of Germany Germany 113 45.75%
2 image of flag of United States United States 26 10.53%
3 image of flag of China China 26 10.53%
4 image of flag of India India 21 8.50%
5 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 6 2.43%
6 image of flag of France France 6 2.43%
7 image of flag of United Kingdom United Kingdom 5 2.02%
8 image of flag of Switzerland Switzerland 5 2.02%
9 image of flag of No geo information available No geo information available 4 1.62%
10 image of flag of Czech Republic Czech Republic 4 1.62%
    other countries 31 12.55%

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