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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 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. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210520-150008009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 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 (RNN) 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:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2104.12980arXivDiscussion Paper
ORCID:
AuthorORCID
Fremling, Christoffer0000-0002-4223-103X
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
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
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: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). MR has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 759194 - USNAC). M. W. C acknowledges support from the National Science Foundation with grant number PHY-2010970. 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
Subject Keywords:(stars:) supernovae: general - methods: data analysis - surveys
Record Number:CaltechAUTHORS:20210520-150008009
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210520-150008009
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:24 May 2021 17:30

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