Gravitational wave denoising of binary black hole mergers with deep learning
Gravitational wave detection requires an in-depth understanding of the physical properties of gravitational wave signals, and the noise from which they are extracted. Understanding the statistical properties of noise is a complex endeavor, particularly in realistic detection scenarios. In this artic...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-01-01
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| Series: | Physics Letters B |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0370269319308032 |
| _version_ | 1811196218162806784 |
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| author | Wei Wei E.A. Huerta |
| author_facet | Wei Wei E.A. Huerta |
| author_sort | Wei Wei |
| collection | DOAJ |
| description | Gravitational wave detection requires an in-depth understanding of the physical properties of gravitational wave signals, and the noise from which they are extracted. Understanding the statistical properties of noise is a complex endeavor, particularly in realistic detection scenarios. In this article we demonstrate that deep learning can handle the non-Gaussian and non-stationary nature of gravitational wave data, and showcase its application to denoise the gravitational wave signals generated by the binary black hole mergers GW150914, GW170104, GW170608 and GW170814 from advanced LIGO noise. To exhibit the accuracy of this methodology, we compute the overlap between the time-series signals produced by our denoising algorithm, and the numerical relativity templates that are expected to describe these gravitational wave sources, finding overlaps O≳0.99. We also show that our deep learning algorithm is capable of removing noise anomalies from numerical relativity signals that we inject in real advanced LIGO data. We discuss the implications of these results for the characterization of gravitational wave signals. Keywords: Gravitational waves, Deep learning, Denoising, Black holes, LIGO |
| first_indexed | 2024-04-12T00:55:02Z |
| format | Article |
| id | doaj.art-b7f29c2cda0c49088bfeedbb3171a093 |
| institution | Directory Open Access Journal |
| issn | 0370-2693 |
| language | English |
| last_indexed | 2024-04-12T00:55:02Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Physics Letters B |
| spelling | doaj.art-b7f29c2cda0c49088bfeedbb3171a0932022-12-22T03:54:38ZengElsevierPhysics Letters B0370-26932020-01-01800Gravitational wave denoising of binary black hole mergers with deep learningWei Wei0E.A. Huerta1NCSA, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Corresponding author.NCSA, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Department of Astronomy, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USAGravitational wave detection requires an in-depth understanding of the physical properties of gravitational wave signals, and the noise from which they are extracted. Understanding the statistical properties of noise is a complex endeavor, particularly in realistic detection scenarios. In this article we demonstrate that deep learning can handle the non-Gaussian and non-stationary nature of gravitational wave data, and showcase its application to denoise the gravitational wave signals generated by the binary black hole mergers GW150914, GW170104, GW170608 and GW170814 from advanced LIGO noise. To exhibit the accuracy of this methodology, we compute the overlap between the time-series signals produced by our denoising algorithm, and the numerical relativity templates that are expected to describe these gravitational wave sources, finding overlaps O≳0.99. We also show that our deep learning algorithm is capable of removing noise anomalies from numerical relativity signals that we inject in real advanced LIGO data. We discuss the implications of these results for the characterization of gravitational wave signals. Keywords: Gravitational waves, Deep learning, Denoising, Black holes, LIGOhttp://www.sciencedirect.com/science/article/pii/S0370269319308032 |
| spellingShingle | Wei Wei E.A. Huerta Gravitational wave denoising of binary black hole mergers with deep learning Physics Letters B |
| title | Gravitational wave denoising of binary black hole mergers with deep learning |
| title_full | Gravitational wave denoising of binary black hole mergers with deep learning |
| title_fullStr | Gravitational wave denoising of binary black hole mergers with deep learning |
| title_full_unstemmed | Gravitational wave denoising of binary black hole mergers with deep learning |
| title_short | Gravitational wave denoising of binary black hole mergers with deep learning |
| title_sort | gravitational wave denoising of binary black hole mergers with deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S0370269319308032 |
| work_keys_str_mv | AT weiwei gravitationalwavedenoisingofbinaryblackholemergerswithdeeplearning AT eahuerta gravitationalwavedenoisingofbinaryblackholemergerswithdeeplearning |