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SNIascore: Deep-learning Classification of Low-resolution Supernova Spectra

Fremling, Christoffer and Hall, Xander J. and Coughlin, Michael W. and Dahiwale, Aishwarya S. and Duev, Dmitry A. and Graham, Matthew J. and Kasliwal, Mansi M. and Kool, Erik C. and Mahabal, Ashish A. and Miller, Adam A. and Neill, James D. and Perley, Daniel A. and Rigault, Mickael and Rosnet, Philippe and Rusholme, Ben and Sharma, Yashvi and Shin, Kyung Min and Shupe, David L. and Sollerman, Jesper and Walters, Richard S. and Kulkarni, S. R. (2021) SNIascore: Deep-learning Classification of Low-resolution Supernova Spectra. Astrophysical Journal Letters, 917 (1). Art. No. L2. ISSN 2041-8205. doi:10.3847/2041-8213/ac116f. https://resolver.caltech.edu/CaltechAUTHORS:20210520-150008009

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Abstract

We present SNIascore, a deep-learning-based method for spectroscopic classification of thermonuclear supernovae (SNe Ia) based on very low-resolution (R ~ 100) data. The goal of SNIascore is the fully automated classification of SNe Ia with a very low false-positive rate (FPR) so that human intervention can be greatly reduced in large-scale SN classification efforts, such as that undertaken by the public Zwicky Transient Facility (ZTF) Bright Transient Survey (BTS). We utilize a recurrent neural network architecture with a combination of bidirectional long short-term memory and gated recurrent unit layers. SNIascore achieves a <0.6% FPR while classifying up to 90% of the low-resolution SN Ia spectra obtained by the BTS. SNIascore simultaneously performs binary classification and predicts the redshifts of secure SNe Ia via regression (with a typical uncertainty of <0.005 in the range from z = 0.01 to z = 0.12). For the magnitude-limited ZTF BTS survey (≈70% SNe Ia), deploying SNIascore reduces the amount of spectra in need of human classification or confirmation by ≈60%. Furthermore, SNIascore allows SN Ia classifications to be automatically announced in real time to the public immediately following a finished observation during the night.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3847/2041-8213/ac116fDOIArticle
https://arxiv.org/abs/2104.12980arXivDiscussion Paper
ORCID:
AuthorORCID
Fremling, Christoffer0000-0002-4223-103X
Hall, Xander J.0000-0002-9364-5419
Coughlin, Michael W.0000-0002-8262-2924
Duev, Dmitry A.0000-0001-5060-8733
Graham, Matthew J.0000-0002-3168-0139
Kasliwal, Mansi M.0000-0002-5619-4938
Kool, Erik C.0000-0002-7252-3877
Mahabal, Ashish A.0000-0003-2242-0244
Miller, Adam A.0000-0001-9515-478X
Neill, James D.0000-0002-0466-1119
Perley, Daniel A.0000-0001-8472-1996
Rigault, Mickael0000-0002-8121-2560
Rosnet, Philippe0000-0002-6099-7565
Rusholme, Ben0000-0001-7648-4142
Sharma, Yashvi0000-0003-4531-1745
Shin, Kyung Min0000-0002-1486-3582
Shupe, David L.0000-0003-4401-0430
Sollerman, Jesper0000-0003-1546-6615
Walters, Richard S.0000-0002-1835-6078
Kulkarni, S. R.0000-0001-5390-8563
Additional Information:© 2021. The American Astronomical Society. Received 2021 April 27; revised 2021 July 3; accepted 2021 July 6; published 2021 August 5. SED Machine is based upon work supported by the National Science Foundation under grant No. 1106171. Based on observations obtained with the Samuel Oschin Telescope 48 inch and the 60 inch Telescope at the Palomar Observatory as part of the Zwicky Transient Facility project. ZTF is supported by the National Science Foundation under grant No. AST-1440341 and a collaboration including Caltech, IPAC, the Weizmann Institute for Science, the Oskar Klein Center at Stockholm University, the University of Maryland, the University of Washington, Deutsches Elektronen-Synchrotron and Humboldt University, Los Alamos National Laboratories, the TANGO Consortium of Taiwan, the University of Wisconsin at Milwaukee, and Lawrence Berkeley National Laboratories. Operations are conducted by COO, IPAC, and UW. This work was supported by the GROWTH project funded by the National Science Foundation under PIRE grant No. 1545949. The Oskar Klein Centre is funded by the Swedish Research Council. C.F. gratefully acknowledges support of his research by the Heising-Simons Foundation (#2018-0907). M.R. has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 759194—USNAC). M.W.C. acknowledges support from the National Science Foundation with grant No. PHY-2010970. Based in part upon work supported by the LSST Corporation (LSSTC), through Enabling Science Grants #2019-UG01 and #2020-01. Facilities: P48 - , P60(SEDM). - Software: MATLAB (MATLAB 2020), SNID (Blondin & Tonry 2007), DASH (Muthukrishna et al. 2019b), the GROWTH Marshal (Kasliwal et al. 2019), Fritz (https://github.com/fritz-marshal/fritz).
Group:Astronomy Department, Infrared Processing and Analysis Center (IPAC), Zwicky Transient Facility
Funders:
Funding AgencyGrant Number
NSFAST-1106171
NSFAST-1440341
ZTF partner institutionsUNSPECIFIED
NSFOISE-1545949
Swedish Research CouncilUNSPECIFIED
Heising-Simons Foundation2018-0907
European Research Council (ERC)759194
NSFPHY-2010970
Large Synoptic Survey Telescope Corporation2019-UG01
Large Synoptic Survey Telescope Corporation2020-01
Subject Keywords:(stars:) supernovae: general - methods: data analysis - surveys
Issue or Number:1
Classification Code:Unified Astronomy Thesaurus concepts: Supernovae (1668); Type Ia supernovae (1728); Neural networks (1933); Classification (1907); Surveys (1671)
DOI:10.3847/2041-8213/ac116f
Record Number:CaltechAUTHORS:20210520-150008009
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210520-150008009
Official Citation:Christoffer Fremling et al 2021 ApJL 917 L2
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109223
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:24 May 2021 17:30
Last Modified:09 Aug 2021 21:07

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