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Source-agnostic gravitational-wave detection with recurrent autoencoders

Moreno, Eric A. and Borzyszkowski, Bartlomiej and Pierini, Maurizio and Vlimant, Jean-Roch and Spiropulu, Maria (2022) Source-agnostic gravitational-wave detection with recurrent autoencoders. Machine Learning: Science and Technology, 3 (2). Art. No. 025001. ISSN 2632-2153. doi:10.1088/2632-2153/ac5385. https://resolver.caltech.edu/CaltechAUTHORS:20211217-233151582

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Abstract

We present an application of anomaly detection techniques based on deep recurrent autoencoders (AEs) to the problem of detecting gravitational wave (GW) signals in laser interferometers. Trained on noise data, this class of algorithms could detect signals using an unsupervised strategy, i.e. without targeting a specific kind of source. We develop a custom architecture to analyze the data from two interferometers. We compare the obtained performance to that obtained with other AE architectures and with a convolutional classifier. The unsupervised nature of the proposed strategy comes with a cost in terms of accuracy, when compared to more traditional supervised techniques. On the other hand, there is a qualitative gain in generalizing the experimental sensitivity beyond the ensemble of pre-computed signal templates. The recurrent AE outperforms other AEs based on different architectures. The class of recurrent AEs presented in this paper could complement the search strategy employed for GW detection and extend the discovery reach of the ongoing detection campaigns.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1088/2632-2153/ac5385DOIArticle
https://arxiv.org/abs/2107.12698arXivDiscussion Paper
https://doi.org/10.5281/zenodo.5121514DOIData
https://doi.org/10.5281/zenodo.5121510DOIData
https://doi.org/10.5281/zenodo.5772814DOIData
https://doi.org/10.5281/zenodo.5773513DOIData
ORCID:
AuthorORCID
Moreno, Eric A.0000-0001-5666-3637
Borzyszkowski, Bartlomiej0000-0002-2927-7009
Pierini, Maurizio0000-0003-1939-4268
Vlimant, Jean-Roch0000-0002-9705-101X
Spiropulu, Maria0000-0001-8172-7081
Additional Information:© 2022 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 13 August 2021; Accepted 9 February 2022; Published 4 April 2022. We are grateful to the insight and expertise of Rana Adhikari, Hang Yu, and Erik Katsavounidis from the LIGO collaboration and Elena Cuoco from the VIRGO collaboration, who guided us on a field of research which is not our own. Part of this work was conducted at 'iBanks', the AI GPU cluster at Caltech. We acknowledge NVIDIA, SuperMicro and the Kavli Foundation for their support of 'iBanks'. This work was carried on as part of the 2020 CERN OpenLab Summer Student program, which was carried on in remote mode due to the COVID pandemic. M P is supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant Agreement No. 772369). E M is supported by the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) through a fellowship in Innovative Algorithms. This work is partially supported by the U.S. DOE, Office of Science, Office of High Energy Physics under Award Nos. DE-SC0011925, DE-SC0019227 and DE-AC02-07CH11359. Data availability statement: The data that support the findings of this study are openly available at the following URL/DOI: 10.5281/zenodo.5121514, 10.5281/zenodo.5121510, 10.5281/zenodo.5772814 and 10.5281/zenodo.5773513.
Funders:
Funding AgencyGrant Number
NVIDIA CorporationUNSPECIFIED
SuperMicro CorporationUNSPECIFIED
Kavli FoundationUNSPECIFIED
European Research Council (ERC)772369
Institute for Research and Innovation in Software for High Energy PhysicsUNSPECIFIED
Department of Energy (DOE)DE-SC0011925
Department of Energy (DOE)DE-SC0019227
Department of Energy (DOE)DE-AC02-07CH11359
Subject Keywords:machine learning, unsupervised learning, anomaly detection
Issue or Number:2
DOI:10.1088/2632-2153/ac5385
Record Number:CaltechAUTHORS:20211217-233151582
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20211217-233151582
Official Citation:Eric A Moreno et al 2022 Mach. Learn.: Sci. Technol. 3 025001
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112532
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:20 Dec 2021 20:34
Last Modified:06 Apr 2022 17:24

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