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Flexible and Accurate Evaluation of Gravitational-wave Malmquist Bias with Machine Learning

Talbot, Colm and Thrane, Eric (2022) Flexible and Accurate Evaluation of Gravitational-wave Malmquist Bias with Machine Learning. Astrophysical Journal, 927 (1). Art. No. 76. ISSN 0004-637X. doi:10.3847/1538-4357/ac4bc0. https://resolver.caltech.edu/CaltechAUTHORS:20220307-188982000

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Abstract

Many astronomical surveys are limited by the brightness of the sources, and gravitational-wave searches are no exception. The detectability of gravitational waves from merging binaries is affected by the mass and spin of the constituent compact objects. To perform unbiased inference on the distribution of compact binaries, it is necessary to account for this selection effect, which is known as Malmquist bias. Since systematic error from selection effects grows with the number of events, it will be increasingly important over the coming years to accurately estimate the observational selection function for gravitational-wave astronomy. We employ density estimation methods to accurately and efficiently compute the compact binary coalescence selection function. We introduce a simple pre-processing method, which significantly reduces the complexity of the required machine-learning models. We demonstrate that our method has smaller statistical errors at comparable computational cost than the method currently most widely used allowing us to probe narrower distributions of spin magnitudes. The currently used method leaves 10%–50% of the interesting black hole spin models inaccessible; our new method can probe >99% of the models and has a lower uncertainty for >80% of the models.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3847/1538-4357/ac4bc0DOIArticle
https://arxiv.org/abs/2012.01317arXivDiscussion Paper
ORCID:
AuthorORCID
Talbot, Colm0000-0003-2053-5582
Thrane, Eric0000-0002-4418-3895
Additional Information:© 2022. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 2021 June 29; revised 2022 January 2; accepted 2022 January 14; published 2022 March 7. We thank Maya Fishbach for producing mock injections used in an early version of this work. We thank Sylvia Biscoveanu, Tom Dent, Reed Essick, Jacob Golomb, Cody Messick, Richard O'Shaughnessy, Alan Weinstein, Daniel Wysocki, and Salvatore Vitale for useful comments and discussions. This work is supported through the Australian Research Council (ARC) Centre of Excellence CE170100004 and ARC Future Fellowship FT150100281. This is document LIGO-P2000505. This research has made use of data, software, and/or web tools obtained from the Gravitational Wave Open Science Center (https://www.gw-openscience.org), a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. Computing was performed computing clusters at the California Institute of Technology (LIGO Laboratory) supported by National Science Foundation Grants PHY-0757058 and PHY-0823459 and Swinburne University of Technology (OzSTAR). This work used publicly available samples from LIGO Scientific Collaboration & Virgo Scientific Collaboration (2018, 2020a, 2020b) and LIGO Scientific Collaboration et al. (2021a, 2021b). A jupyter notebook to fully reproduce the results presented here along with a number of additional diagnostic figures can be found on Github. 8 This work made use of Google Colaboratory. Software used in this work includes: numpy (Harris et al. 2020), scipy (Virtanen et al. 2020), scikit-learn (Pedregosa et al. 2011), matplotlib (Hunter 2007), pandas (pandas development team, T., 2020), cupy (Okuta et al. 2017), Bilby (Ashton et al. 2019; Ashton & Talbot 2021), and GWPopulation (Talbot et al. 2019).
Group:LIGO
Funders:
Funding AgencyGrant Number
Australian Research CouncilCE170100004
Australian Research CouncilFT150100281
NSFPHY-0757058
NSFPHY-0823459
Swinburne University of TechnologyUNSPECIFIED
Subject Keywords:Gravitational waves; Gravitational wave astronomy; Gravitational wave sources
Other Numbering System:
Other Numbering System NameOther Numbering System ID
LIGO DocumentP2000505
Issue or Number:1
Classification Code:Unified Astronomy Thesaurus concepts: Gravitational waves (678); Gravitational wave astronomy (675); Gravitational wave sources (677)
DOI:10.3847/1538-4357/ac4bc0
Record Number:CaltechAUTHORS:20220307-188982000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220307-188982000
Official Citation:Colm Talbot and Eric Thrane 2022 ApJ 927 76
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113771
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:08 Mar 2022 23:38
Last Modified:08 Mar 2022 23:38

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