Superphot+: Realtime Fitting and Classification of Supernova Light Curves
Kaylee M. de Soto (1), Ashley Villar (1), Edo Berger (1, 2),, Sebastian Gomez (3), Griffin Hosseinzadeh (4), Doug Branton (5), Sandro, Campos (6), Melissa DeLucchi (6), Jeremy Kubica (6), Olivia Lynn (6),, Konstantin Malanchev (6), Alex I. Malz (6) ((1) Center for Astrophysics |

TL;DR
Superphot+ is a real-time photometric classifier for supernova light curves that achieves high accuracy without relying on redshift data, using a parametric model and gradient boosting.
Contribution
It introduces Superphot+, a novel package that classifies supernovae from multiband light curves without redshift, matching or surpassing existing methods in accuracy.
Findings
Achieves 0.83 accuracy without redshift
Improves to 0.88 accuracy with redshift
Classifies ZTF SNe in real time
Abstract
Photometric classifications of supernova (SN) light curves have become necessary to utilize the full potential of large samples of observations obtained from wide-field photometric surveys, such as the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory. Here, we present a photometric classifier for SN light curves that does not rely on redshift information and still maintains comparable accuracy to redshift-dependent classifiers. Our new package, Superphot+, uses a parametric model to extract meaningful features from multiband SN light curves. We train a gradient-boosted machine with fit parameters from 6,061 ZTF SNe that pass data quality cuts and are spectroscopically classified as one of five classes: SN Ia, SN II, SN Ib/c, SN IIn, and SLSN-I. Without redshift information, our classifier yields a class-averaged F1-score of 0.61 +/- 0.02 and a total accuracy of 0.83 +/-…
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Taxonomy
TopicsGamma-ray bursts and supernovae · Astrophysics and Cosmic Phenomena · Astronomy and Astrophysical Research
