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

V. Ashley Villar*, Griffin Hosseinzadeh, Edo Berger, Michelle Ntampaka, David O. Jones, Peter Challis, Ryan Chornock, Maria R. Drout, Ryan J. Foley, Robert P. Kirshner, Ragnhild Lunnan, Raffaella Margutti, Dan Milisavljevic, Nathan Sanders, Yen Chen Pan, Armin Rest, Daniel M. Scolnic, Eugene Magnier, Nigel Metcalfe, Richard WainscoatChristopher Waters

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Automated classification of supernovae (SNe) based on optical photometric light-curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin Observatory. Photometric classification can enable real-time identification of interesting events for extended multiwavelength follow-up, as well as archival population studies. Here we present the complete sample of 5243 "SN-like"light curves (in g P1 r P1 i P1 z P1) from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS). The PS1-MDS is similar to the planned LSST Wide-Fast-Deep survey in terms of cadence, filters, and depth, making this a useful training set for the community. Using this data set, we train a novel semisupervised machine learning algorithm to photometrically classify 2315 new SN-like light curves with host galaxy spectroscopic redshifts. Our algorithm consists of an RF supervised classification step and a novel unsupervised step in which we introduce a recurrent autoencoder neural network (RAENN). Our final pipeline, dubbed SuperRAENN, has an accuracy of 87% across five SN classes (Type Ia, Ibc, II, IIn, SLSN-I) and macro-averaged purity and completeness of 66% and 69%, respectively. We find the highest accuracy rates for SNe Ia and SLSNe and the lowest for SNe Ibc. Our complete spectroscopically and photometrically classified samples break down into 62.0% Type Ia (1839 objects), 19.8% Type II (553 objects), 4.8% Type IIn (136 objects), 11.7% Type Ibc (291 objects), and 1.6% Type I SLSNe (54 objects).

Original languageEnglish (US)
Article number94
JournalAstrophysical Journal
Volume905
Issue number2
DOIs
StatePublished - Dec 20 2020

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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