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Nonlinear Noise Cleaning in Gravitational-Wave Detectors With Convolutional Neural Networks

Yu, Hang and Adhikari, Rana X. (2022) Nonlinear Noise Cleaning in Gravitational-Wave Detectors With Convolutional Neural Networks. Frontiers in Artificial Intelligence, 5 . Art. No. 811563. ISSN 2624-8212. PMCID PMC8969740. doi:10.3389/frai.2022.811563.

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Currently, the sub-60 Hz sensitivity of gravitational-wave (GW) detectors like Advanced LIGO (aLIGO) is limited by the control noises from auxiliary degrees of freedom which nonlinearly couple to the main GW readout. One promising way to tackle this challenge is to perform nonlinear noise mitigation using convolutional neural networks (CNNs), which we examine in detail in this study. In many cases, the noise coupling is bilinear and can be viewed as a few fast channels' outputs modulated by some slow channels. We show that we can utilize this knowledge of the physical system and adopt an explicit “slow×fast” structure in the design of the CNN to enhance its performance of noise subtraction. We then examine the requirements in the signal-to-noise ratio (SNR) in both the target channel (i.e., the main GW readout) and in the auxiliary sensors in order to reduce the noise by at least a factor of a few. In the case of limited SNR in the target channel, we further demonstrate that the CNN can still reach a good performance if we use curriculum learning techniques, which in reality can be achieved by combining data from quiet times and those from periods with active noise injections.

Item Type:Article
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URLURL TypeDescription CentralArticle
Yu, Hang0000-0002-6011-6190
Adhikari, Rana X.0000-0002-5731-5076
Additional Information:© 2022 Yu and Adhikari. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Received: 09 November 2021; Accepted: 08 February 2022; Published: 17 March 2022. We thank Gabriele Vajente and Szabolcs Marka for helpful discussions and comments during the preparation of this manuscript, and we acknowledge feedback from fellow participants in the LIGO Machine Learning Algorithms Call. HY is supported by the Sherman Fairchild Foundation. RA is supported by NSF grant no. PHY-1764464. The authors gratefully acknowledge the computational resources provided by the LIGO Laboratory and supported by NSF grant nos. PHY-0757058 and PHY-0823459. Author Contributions. HY performed the training and testing of the CNNs. RA supervised the research. Both authors contributed to the article and approved the submitted version. Data Availability Statement. The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Group:Astronomy Department, LIGO, Walter Burke Institute for Theoretical Physics
Funding AgencyGrant Number
Sherman Fairchild FoundationUNSPECIFIED
Subject Keywords:gravitational-wave detectors, Advanced LIGO, noise regression, machine learning, neural networks
PubMed Central ID:PMC8969740
Record Number:CaltechAUTHORS:20220414-27186000
Persistent URL:
Official Citation:Yu H and Adhikari RX (2022) Nonlinear Noise Cleaning in Gravitational-Wave Detectors With Convolutional Neural Networks. Front. Artif. Intell. 5:811563. doi: 10.3389/frai.2022.811563
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:114322
Deposited By: George Porter
Deposited On:15 Apr 2022 16:29
Last Modified:15 Apr 2022 16:29

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