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Title: Photometric Classification of Early-time Supernova Light Curves with SCONE

Abstract

Abstract In this work, we present classification results on early supernova light curves from SCONE, a photometric classifier that uses convolutional neural networks to categorize supernovae (SNe) by type using light-curve data. SCONE is able to identify SN types from light curves at any stage, from the night of initial alert to the end of their lifetimes. Simulated LSST SNe light curves were truncated at 0, 5, 15, 25, and 50 days after the trigger date and used to train Gaussian processes in wavelength and time space to produce wavelength–time heatmaps. SCONE uses these heatmaps to perform six-way classification between SN types Ia, II, Ibc, Ia-91bg, Iax, and SLSN-I. SCONE is able to perform classification with or without redshift, but we show that incorporating redshift information improves performance at each epoch. SCONE achieved 75% overall accuracy at the date of trigger (60% without redshift), and 89% accuracy 50 days after trigger (82% without redshift). SCONE was also tested on bright subsets of SNe ( r < 20 mag) and produced 91% accuracy at the date of trigger (83% without redshift) and 95% five days after trigger (94.7% without redshift). SCONE is the first application of convolutional neural networks to themore » early-time photometric transient classification problem. All of the data processing and model code developed for this paper can be found in the SCONE software package 1 1 github.com/helenqu/scone located at github.com/helenqu/scone (Qu 2021).« less

Authors:
ORCiD logo; ORCiD logo
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC); National Aeronautics and Space Administration (NASA); National Science Foundation (NSF)
OSTI Identifier:
1840079
Alternate Identifier(s):
OSTI ID: 1983151
Grant/Contract Number:  
AC02-05CH11231; FOA-0002424; NNH15ZDA001N; AST-2108094
Resource Type:
Published Article
Journal Name:
The Astronomical Journal
Additional Journal Information:
Journal Name: The Astronomical Journal Journal Volume: 163 Journal Issue: 2; Journal ID: ISSN 0004-6256
Publisher:
American Astronomical Society
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; Photometry; Light curves; Supernovae; Classification; Gaussian process regression; Neural networks

Citation Formats

Qu, Helen, and Sako, Masao. Photometric Classification of Early-time Supernova Light Curves with SCONE. United States: N. p., 2022. Web. doi:10.3847/1538-3881/ac39a1.
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Qu, Helen, & Sako, Masao. Photometric Classification of Early-time Supernova Light Curves with SCONE. United States. https://doi.org/10.3847/1538-3881/ac39a1
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Qu, Helen, and Sako, Masao. Tue . "Photometric Classification of Early-time Supernova Light Curves with SCONE". United States. https://doi.org/10.3847/1538-3881/ac39a1.
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@article{osti_1840079,
title = {Photometric Classification of Early-time Supernova Light Curves with SCONE},
author = {Qu, Helen and Sako, Masao},
abstractNote = {Abstract In this work, we present classification results on early supernova light curves from SCONE, a photometric classifier that uses convolutional neural networks to categorize supernovae (SNe) by type using light-curve data. SCONE is able to identify SN types from light curves at any stage, from the night of initial alert to the end of their lifetimes. Simulated LSST SNe light curves were truncated at 0, 5, 15, 25, and 50 days after the trigger date and used to train Gaussian processes in wavelength and time space to produce wavelength–time heatmaps. SCONE uses these heatmaps to perform six-way classification between SN types Ia, II, Ibc, Ia-91bg, Iax, and SLSN-I. SCONE is able to perform classification with or without redshift, but we show that incorporating redshift information improves performance at each epoch. SCONE achieved 75% overall accuracy at the date of trigger (60% without redshift), and 89% accuracy 50 days after trigger (82% without redshift). SCONE was also tested on bright subsets of SNe ( r < 20 mag) and produced 91% accuracy at the date of trigger (83% without redshift) and 95% five days after trigger (94.7% without redshift). SCONE is the first application of convolutional neural networks to the early-time photometric transient classification problem. All of the data processing and model code developed for this paper can be found in the SCONE software package 1 1 github.com/helenqu/scone located at github.com/helenqu/scone (Qu 2021).},
doi = {10.3847/1538-3881/ac39a1},
journal = {The Astronomical Journal},
number = 2,
volume = 163,
place = {United States},
year = {Tue Jan 11 00:00:00 EST 2022},
month = {Tue Jan 11 00:00:00 EST 2022}
}
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https://doi.org/10.3847/1538-3881/ac39a1
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Works referenced in this record:

Alert Classification for the ALeRCE Broker System: The Light Curve Classifier
journal, February 2021

  • Sánchez-Sáez, P.; Reyes, I.; Valenzuela, C.
  • The Astronomical Journal, Vol. 161, Issue 3
  • DOI: 10.3847/1538-3881/abd5c1

Rapidly evolving transients in the Dark Energy Survey
journal, August 2018

  • Pursiainen, M.; Childress, M.; Smith, M.
  • Monthly Notices of the Royal Astronomical Society, Vol. 481, Issue 1
  • DOI: 10.1093/mnras/sty2309

Photometric Selection of High-Redshift Type Ia Supernova Candidates
journal, February 2006

  • Sullivan, M.; Howell, D. A.; Perrett, K.
  • The Astronomical Journal, Vol. 131, Issue 2
  • DOI: 10.1086/499302

Multilayer feedforward networks are universal approximators
journal, January 1989

PHOTOMETRIC TYPE Ia SUPERNOVA CANDIDATES FROM THE THREE-YEAR SDSS-II SN SURVEY DATA
journal, August 2011

The Sloan Digital sky Survey-Ii Supernova Survey: Search Algorithm and Follow-Up Observations
journal, December 2007

Alert Classification for the ALeRCE Broker System: The Real-time Stamp Classifier
journal, November 2021

  • Carrasco-Davis, R.; Reyes, E.; Valenzuela, C.
  • The Astronomical Journal, Vol. 162, Issue 6
  • DOI: 10.3847/1538-3881/ac0ef1

Extending Supernova Spectral Templates for Next-generation Space Telescope Observations
journal, October 2018

  • Pierel, J. D. R.; Rodney, S.; Avelino, A.
  • Publications of the Astronomical Society of the Pacific, Vol. 130, Issue 993
  • DOI: 10.1088/1538-3873/aadb7a

SN2017jgh: a high-cadence complete shock cooling light curve of a SN IIb with the Kepler telescope
journal, August 2021

  • Armstrong, P.; Tucker, B. E.; Rest, A.
  • Monthly Notices of the Royal Astronomical Society, Vol. 507, Issue 3
  • DOI: 10.1093/mnras/stab2138

Optical Spectra of 73 Stripped-Envelope Core-Collapse Supernovae
journal, March 2014

First cosmology results using type Ia supernovae from the Dark Energy Survey: the effect of host galaxy properties on supernova luminosity
journal, April 2020

  • Smith, M.; Sullivan, M.; Wiseman, P.
  • Monthly Notices of the Royal Astronomical Society, Vol. 494, Issue 3
  • DOI: 10.1093/mnras/staa946

The Supernova Legacy Survey 3-year sample: Type Ia supernovae photometric distances and cosmological constraints
journal, November 2010

RAPID: Early Classification of Explosive Transients Using Deep Learning
journal, September 2019

  • Muthukrishna, Daniel; Narayan, Gautham; Mandel, Kaisey S.
  • Publications of the Astronomical Society of the Pacific, Vol. 131, Issue 1005
  • DOI: 10.1088/1538-3873/ab1609

Supernova Light Curves Powered by Young Magnetars
journal, June 2010

The Magnetar Model for Type I Superluminous Supernovae. I. Bayesian Analysis of the Full Multicolor Light-curve Sample with MOSFiT
journal, November 2017

  • Nicholl, Matt; Guillochon, James; Berger, Edo
  • The Astrophysical Journal, Vol. 850, Issue 1
  • DOI: 10.3847/1538-4357/aa9334

Results from the Supernova Photometric Classification Challenge
journal, December 2010

  • Kessler, Richard; Bassett, Bruce; Belov, Pavel
  • Publications of the Astronomical Society of the Pacific, Vol. 122, Issue 898
  • DOI: 10.1086/657607

A faint type of supernova from a white dwarf with a helium-rich companion
journal, May 2010

  • Perets, H. B.; Gal-Yam, A.; Mazzali, P. A.
  • Nature, Vol. 465, Issue 7296
  • DOI: 10.1038/nature09056

Constraining Dark Energy with Type Ia Supernovae and Large-Scale Structure
journal, July 1999

LSST: From Science Drivers to Reference Design and Anticipated Data Products
journal, March 2019

  • Ivezić, Željko; Kahn, Steven M.; Tyson, J. Anthony
  • The Astrophysical Journal, Vol. 873, Issue 2
  • DOI: 10.3847/1538-4357/ab042c

Backpropagation Applied to Handwritten Zip Code Recognition
journal, December 1989

PELICAN: deeP architecturE for the LIght Curve ANalysis
journal, June 2019

Bayesian Single-Epoch Photometric Classification of Supernovae
journal, July 2007

  • Poznanski, Dovi; Maoz, Dan; Gal-Yam, Avishay
  • The Astronomical Journal, Vol. 134, Issue 3
  • DOI: 10.1086/520956

Deep multi-survey classification of variable stars
journal, November 2018

  • Aguirre, C.; Pichara, K.; Becker, I.
  • Monthly Notices of the Royal Astronomical Society, Vol. 482, Issue 4
  • DOI: 10.1093/mnras/sty2836

Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation
journal, December 2019

A simple and robust method for automated photometric classification of supernovae using neural networks
journal, December 2012

  • Karpenka, N. V.; Feroz, F.; Hobson, M. P.
  • Monthly Notices of the Royal Astronomical Society, Vol. 429, Issue 2
  • DOI: 10.1093/mnras/sts412

Theoretical Models of Optical Transients. I. A Broad Exploration of the Duration–Luminosity Phase Space
journal, November 2017

  • Villar, V. Ashley; Berger, Edo; Metzger, Brian D.
  • The Astrophysical Journal, Vol. 849, Issue 1
  • DOI: 10.3847/1538-4357/aa8fcb

SNANA: A Public Software Package for Supernova Analysis
journal, September 2009

  • Kessler, Richard; Bernstein, Joseph P.; Cinabro, David
  • Publications of the Astronomical Society of the Pacific, Vol. 121, Issue 883
  • DOI: 10.1086/605984

TESTING MODELS OF INTRINSIC BRIGHTNESS VARIATIONS IN TYPE Ia SUPERNOVAE AND THEIR IMPACT ON MEASURING COSMOLOGICAL PARAMETERS
journal, January 2013

SuperNNova: an open-source framework for Bayesian, neural network-based supernova classification
journal, December 2019

  • Möller, A.; de Boissière, T.
  • Monthly Notices of the Royal Astronomical Society, Vol. 491, Issue 3
  • DOI: 10.1093/mnras/stz3312

A recurrent neural network for classification of unevenly sampled variable stars
journal, November 2017

SuperRAENN: A Semisupervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium-Deep Survey Supernovae
journal, December 2020

  • Villar, V. Ashley; Hosseinzadeh, Griffin; Berger, Edo
  • The Astrophysical Journal, Vol. 905, Issue 2
  • DOI: 10.3847/1538-4357/abc6fd

The Sloan Digital sky Survey-Ii Supernova Survey: Technical Summary
journal, December 2007

Large Magellanic Cloud Cepheid Standards Provide a 1% Foundation for the Determination of the Hubble Constant and Stronger Evidence for Physics beyond ΛCDM
journal, May 2019

  • Riess, Adam G.; Casertano, Stefano; Yuan, Wenlong
  • The Astrophysical Journal, Vol. 876, Issue 1
  • DOI: 10.3847/1538-4357/ab1422

Gradient-based learning applied to document recognition
journal, January 1998

  • Lecun, Y.; Bottou, L.; Bengio, Y.
  • Proceedings of the IEEE, Vol. 86, Issue 11
  • DOI: 10.1109/5.726791

ParSNIP: Generative Models of Transient Light Curves with Physics-enabled Deep Learning
journal, December 2021

Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
journal, September 1998

  • Riess, Adam G.; Filippenko, Alexei V.; Challis, Peter
  • The Astronomical Journal, Vol. 116, Issue 3
  • DOI: 10.1086/300499

The Carnegie-Chicago Hubble Program. VIII. An Independent Determination of the Hubble Constant Based on the Tip of the Red Giant Branch
journal, August 2019

  • Freedman, Wendy L.; Madore, Barry F.; Hatt, Dylan
  • The Astrophysical Journal, Vol. 882, Issue 1
  • DOI: 10.3847/1538-4357/ab2f73

Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)
journal, July 2019

  • Kessler, R.; Narayan, G.; Avelino, A.
  • Publications of the Astronomical Society of the Pacific, Vol. 131, Issue 1003
  • DOI: 10.1088/1538-3873/ab26f1

Long Short-Term Memory
journal, November 1997

Semi-supervised learning for photometric supernova classification★: Semi-supervised SN classification
journal, October 2011

  • Richards, Joseph W.; Homrighausen, Darren; Freeman, Peter E.
  • Monthly Notices of the Royal Astronomical Society, Vol. 419, Issue 2
  • DOI: 10.1111/j.1365-2966.2011.19768.x

MOSFiT: Modular Open Source Fitter for Transients
journal, May 2018

  • Guillochon, James; Nicholl, Matt; Villar, V. Ashley
  • The Astrophysical Journal Supplement Series, Vol. 236, Issue 1
  • DOI: 10.3847/1538-4365/aab761

SCONE: Supernova Classification with a Convolutional Neural Network
journal, July 2021

Supernova 1987A in the Large Magellanic Cloud - The explosion of an approximately 20 solar mass star which has experienced mass loss?
journal, July 1987

  • Woosley, S. E.; Pinto, P. A.; Martin, P. G.
  • The Astrophysical Journal, Vol. 318
  • DOI: 10.1086/165402

Deep Recurrent Neural Networks for Supernovae Classification
journal, March 2017

The Type II supernova SN 2020jfo in M 61, implications for progenitor system, and explosion dynamics
journal, November 2021

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