Metric Assisted Stochastic Sampling (MASS) search for gravitational waves from binary black hole mergers
We present a novel gravitational wave detection algorithm that conducts a matched filter search stochastically across the compact binary parameter space rather than relying on a fixed bank of template waveforms. This technique is competitive with standard template-bank-driven pipelines in both compu...
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| Veröffentlicht in: | arXiv.org 2022-10 |
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| creator | Hanna, Chad Joshi, Prathamesh Huxford, Rachael Cannon, Kipp Caudill, Sarah Chan, Chiwai Cousins, Bryce Creighton, Jolien D E Ewing, Becca Fernandez, Miguel Fong, Heather Godwin, Patrick Magee, Ryan Meacher, Duncan Messick, Cody Morisaki, Soichiro Mukherjee, Debnandini Ohta, Hiroaki Pace, Alexander Privitera, Stephen Sachdev, Surabhi Shio Sakon Singh, Divya Tapia, Ron Tsukada, Leo Tsuna, Daichi Tsutsui, Takuya Ueno, Koh Viets, Aaron Wade, Leslie Wade, Madeline Wang, Jonathan |
| description | We present a novel gravitational wave detection algorithm that conducts a matched filter search stochastically across the compact binary parameter space rather than relying on a fixed bank of template waveforms. This technique is competitive with standard template-bank-driven pipelines in both computational cost and sensitivity. However, the complexity of the analysis is simpler allowing for easy configuration and horizontal scaling across heterogeneous grids of computers. To demonstrate the method we analyze approximately one month of public LIGO data from July 27 00:00 2017 UTC - Aug 25 22:00 2017 UTC and recover eight known confident gravitational wave candidates. We also inject simulated binary black hole (BBH) signals to demonstrate the sensitivity. |
| doi_str_mv | 10.48550/arxiv.2110.15463 |
| format | Article |
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This technique is competitive with standard template-bank-driven pipelines in both computational cost and sensitivity. However, the complexity of the analysis is simpler allowing for easy configuration and horizontal scaling across heterogeneous grids of computers. To demonstrate the method we analyze approximately one month of public LIGO data from July 27 00:00 2017 UTC - Aug 25 22:00 2017 UTC and recover eight known confident gravitational wave candidates. We also inject simulated binary black hole (BBH) signals to demonstrate the sensitivity.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2110.15463</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Algorithms ; Approximation ; Cost analysis ; Gravitational waves ; Matched filters ; Physics - General Relativity and Quantum Cosmology ; Waveforms</subject><ispartof>arXiv.org, 2022-10</ispartof><rights>2022. 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| subjects | Algorithms Approximation Cost analysis Gravitational waves Matched filters Physics - General Relativity and Quantum Cosmology Waveforms |
| title | Metric Assisted Stochastic Sampling (MASS) search for gravitational waves from binary black hole mergers |
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