CaltechAUTHORS
Published August 10, 2021 | Version Submitted + Published
Journal Article Open

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

Creators

  • 1. ROR icon California Institute of Technology
  • 2. ROR icon University of Minnesota
  • 3. ROR icon Stockholm University
  • 4. ROR icon Northwestern University
  • 5. ROR icon Adler Planetarium
  • 6. ROR icon Liverpool John Moores University
  • 7. ROR icon Claude Bernard University Lyon 1
  • 8. ROR icon University of Clermont Auvergne
  • 9. ROR icon Infrared Processing and Analysis Center

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.

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).

Attached Files

Published - Fremling_2021_ApJL_917_L2.pdf

Submitted - 2104.12980.pdf

Files

2104.12980.pdf

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Additional details

Identifiers

Eprint ID
109223
Resolver ID
CaltechAUTHORS:20210520-150008009

Related works

Describes
https://arxiv.org/abs/2104.12980 (URL)
https://www.caltech.edu/about/news/machine-learning-tools-automatically-classify-1000-supernovae (URL)

Funding

NSF
AST-1106171
NSF
AST-1440341
ZTF partner institutions
NSF
OISE-1545949
Swedish Research Council
Heising-Simons Foundation
2018-0907
European Research Council (ERC)
759194
NSF
PHY-2010970
Large Synoptic Survey Telescope Corporation
2019-UG01
Large Synoptic Survey Telescope Corporation
2020-01

Dates

Created
2021-05-24
Created from EPrint's datestamp field
Updated
2023-02-27
Created from EPrint's last_modified field

Caltech Custom Metadata

Caltech groups
Astronomy Department, Infrared Processing and Analysis Center (IPAC), Zwicky Transient Facility, Division of Geological and Planetary Sciences (GPS)