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Supernova Photometric Classification Pipelines Trained on Spectroscopically Classified Supernovae from the Pan-STARRS1 Medium-deep Survey
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Number of Authors: 142019 (English)In: Astrophysical Journal, ISSN 0004-637X, E-ISSN 1538-4357, Vol. 884, no 1, article id 83Article in journal (Refereed) Published
Abstract [en]

Photometric classification of supernovae (SNe) is imperative as recent and upcoming optical time-domain surveys, such as the Large Synoptic Survey Telescope (LSST), overwhelm the available resources for spectrosopic follow-up. Here we develop a range of light curve (LC) classification pipelines, trained on 513 spectroscopically classified SNe from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS): 357 Type Ia, 93 Type II, 25 Type IIn, 21 Type Ibc, and 17 Type I superluminous SNe (SLSNe). We present a new parametric analytical model that can accommodate a broad range of SN LC morphologies, including those with a plateau, and fit this model to data in four PS1 filters (g(P1)r(P1)i(P1)z(P1)). We test a number of feature extraction methods, data augmentation strategies, and machine-learning algorithms to predict the class of each SN. Our best pipelines result in approximate to 90% average accuracy, approximate to 70% average purity, and approximate to 80% average completeness for all SN classes, with the highest success rates for SNe Ia and SLSNe and the lowest for SNe Ibc. Despite the greater complexity of our classification scheme, the purity of our SN Ia classification, approximate to 95%, is on par with methods developed specifically for Type Ia versus non-Type Ia binary classification. As the first of its kind, this study serves as a guide to developing and training classification algorithms for a wide range of SN types with a purely empirical training set, particularly one that is similar in its characteristics to the expected LSST main survey strategy. Future work will implement this classification pipeline on approximate to 3000 PS1/MDS LCs that lack spectroscopic classification.

Place, publisher, year, edition, pages
2019. Vol. 884, no 1, article id 83
Keywords [en]
supernovae: general, surveys, techniques: photometric
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:su:diva-177524DOI: 10.3847/1538-4357/ab418cISI: 000501733600008OAI: oai:DiVA.org:su-177524DiVA, id: diva2:1383776
Available from: 2020-01-08 Created: 2020-01-08 Last updated: 2020-01-08Bibliographically approved

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Chornock, R.Lunnan, Ragnhild
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Department of AstronomyThe Oskar Klein Centre for Cosmo Particle Physics (OKC)
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