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Using machine learning for transient classification in searches for gravitational-wave counterparts

Stachie, Cosmin and Coughlin, Michael W. and Christensen, Nelson and Muthukrishna, Daniel (2020) Using machine learning for transient classification in searches for gravitational-wave counterparts. Monthly Notices of the Royal Astronomical Society, 497 (2). pp. 1320-1331. ISSN 0035-8711. https://resolver.caltech.edu/CaltechAUTHORS:20200414-084544334

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Abstract

The large sky localization regions offered by the gravitational-wave interferometers require efficient follow-up of the many counterpart candidates identified by the wide field-of-view telescopes. Given the restricted telescope time, the creation of prioritized lists of the many identified candidates becomes mandatory. Towards this end, we use astrorapid, a multiband photometric light-curve classifier, to differentiate between kilonovae, supernovae, and other possible transients. We demonstrate our method on the photometric observations of real events. In addition, the classification performance is tested on simulated light curves, both ideally and realistically sampled. We show that after only a few days of observations of an astronomical object, it is possible to rule out candidates as supernovae and other known transients.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1093/mnras/staa1776DOIArticle
https://arxiv.org/abs/1912.06383arXivDiscussion Paper
https://github.com/mcoughlin/gwemlightcurvesRelated ItemCode
ORCID:
AuthorORCID
Stachie, Cosmin0000-0002-5723-2342
Coughlin, Michael W.0000-0002-8262-2924
Christensen, Nelson0000-0002-6870-4202
Muthukrishna, Daniel0000-0002-5788-9280
Alternate Title:Differentiating the signal from the noise: towards optimal choices of wide field-of-view telescope transient follow-up
Additional Information:© 2020 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model). Accepted 2020 June 13. Received 2020 June 12; in original form 2019 December 12. MC is supported by the David and Ellen Lee Prize Postdoctoral Fellowship at the California Institute of Technology. The authors thank the Observatoire de la Côte d’Azur for support. Data Availability: The data underlying this article are derived from public code found here: https://github.com/mcoughlin/gwemlightcurves. The simulations resulting will be shared on reasonable request to the corresponding author.
Funders:
Funding AgencyGrant Number
David and Ellen Lee Postdoctoral ScholarshipUNSPECIFIED
Observatoire de la Côte d’AzurUNSPECIFIED
Subject Keywords:gravitational waves
Issue or Number:2
Record Number:CaltechAUTHORS:20200414-084544334
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200414-084544334
Official Citation:Cosmin Stachie, Michael W Coughlin, Nelson Christensen, Daniel Muthukrishna, Using machine learning for transient classification in searches for gravitational-wave counterparts, Monthly Notices of the Royal Astronomical Society, Volume 497, Issue 2, September 2020, Pages 1320–1331, https://doi.org/10.1093/mnras/staa1776
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:102524
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:14 Apr 2020 15:50
Last Modified:23 Oct 2020 18:34

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