Photometric Classification of Early-time Supernova Light Curves with SCONE

Journal Article · 10 January 2022 · The Astronomical Journal

DOI: https://doi.org/10.3847/1538-3881/ac39a1 · OSTI ID:1840079

Qu, Helen ; Sako, Masao

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).

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
• Sponsoring Organization: National Aeronautics and Space Administration (NASA); National Science Foundation (NSF); USDOE; USDOE Office of Science (SC)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1840079
• Journal Information: The Astronomical Journal, Journal Name: The Astronomical Journal Journal Issue: 2 Vol. 163; ISSN 0004-6256
• Publisher: American Astronomical SocietyCopyright Statement
• Country of Publication: United States
• Language: English