Revisiting the evidence for precession in GW200129 with machine learning noise mitigation
Revisiting the evidence for precession in GW200129 with machine learning noise mitigation
GW200129 is claimed to be the first-ever observation of the spin-disk orbital precession detected with gravitational waves (GWs) from an individual binary system. However, this claim warrants a cautious evaluation because the GW event coincided with a broadband noise disturbance in LIGO Livingston caused by the 45 MHz electro-optic modulator system. In this paper, we present a state-of-the-art neural network that is able to model and mitigate the broadband noise from the LIGO Livingston interferometer. We also demonstrate that our neural network mitigates the noise better than the algorithm used by the LIGO-Virgo-KAGRA collaboration. Finally, we re-analyse GW200129 with the improved data quality and show that the evidence for precession is still observed.
gr-qc, astro-ph.IM
Macas, Ronaldas
dd6e7dda-1003-4cda-8a1f-cfdeae1804ce
Lundgren, Andrew
a5ed2dd3-1d57-4c09-b7bc-e2959a774582
Ashton, Gregory
a8cec4b1-3c98-4b28-af2a-1e37cb3b9f2a
18 March 2024
Macas, Ronaldas
dd6e7dda-1003-4cda-8a1f-cfdeae1804ce
Lundgren, Andrew
a5ed2dd3-1d57-4c09-b7bc-e2959a774582
Ashton, Gregory
a8cec4b1-3c98-4b28-af2a-1e37cb3b9f2a
Macas, Ronaldas, Lundgren, Andrew and Ashton, Gregory
(2024)
Revisiting the evidence for precession in GW200129 with machine learning noise mitigation.
Physical Review D, 109, [062006].
(doi:10.1103/PhysRevD.109.062006).
Abstract
GW200129 is claimed to be the first-ever observation of the spin-disk orbital precession detected with gravitational waves (GWs) from an individual binary system. However, this claim warrants a cautious evaluation because the GW event coincided with a broadband noise disturbance in LIGO Livingston caused by the 45 MHz electro-optic modulator system. In this paper, we present a state-of-the-art neural network that is able to model and mitigate the broadband noise from the LIGO Livingston interferometer. We also demonstrate that our neural network mitigates the noise better than the algorithm used by the LIGO-Virgo-KAGRA collaboration. Finally, we re-analyse GW200129 with the improved data quality and show that the evidence for precession is still observed.
Text
2311.09921v2
- Accepted Manuscript
More information
Accepted/In Press date: 28 February 2024
Published date: 18 March 2024
Keywords:
gr-qc, astro-ph.IM
Identifiers
Local EPrints ID: 508248
URI: http://eprints.soton.ac.uk/id/eprint/508248
ISSN: 1550-7998
PURE UUID: 576258a4-24b2-458e-92ef-65dd836a7c11
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Date deposited: 15 Jan 2026 17:41
Last modified: 17 Jan 2026 03:47
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Contributors
Author:
Ronaldas Macas
Author:
Andrew Lundgren
Author:
Gregory Ashton
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