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

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

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 gP1rP1iP1zP1) 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).

Citation

Villar, V. A., Hosseinzadeh, G., Berger, E., Ntampaka, M., Jones, D. O., Challis, P., …Waters, C. (2020). SuperRAENN: A Semisupervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium-Deep Survey Supernovae. Astrophysical Journal, 905(2), Article 94. https://doi.org/10.3847/1538-4357/abc6fd

• Journal Article Type: Article
• Acceptance Date: Oct 28, 2020
• Online Publication Date: Dec 20, 2020
• Publication Date: 2020
• Deposit Date: Oct 8, 2021
• Publicly Available Date: Nov 5, 2021
• Journal: Astrophysical Journal
• Print ISSN: 0004-637X
• Publisher: American Astronomical Society
• Peer Reviewed: Peer Reviewed
• Volume: 905
• Issue: 2
• Article Number: 94
• DOI: https://doi.org/10.3847/1538-4357/abc6fd
• Related Public URLs: https://arxiv.org/abs/2008.04921