Photometric Classification of 2315 Pan-STARRS1 Supernovae with Superphot

Abstract

The classification of supernovae (SNe) and its impact on our understanding of the explosion physics and progenitors have traditionally been based on the presence or absence of certain spectral features. However, current and upcoming wide-field time-domain surveys have increased the transient discovery rate far beyond our capacity to obtain even a single spectrum of each new event. We must therefore rely heavily on photometric classification, connecting SN light curves back to their spectroscopically defined classes. Here we present Superphot, an open-source Python implementation of the machine-learning classification algorithm of Villar et al., and apply it to 2315 previously unclassified transients from the Pan-STARRS1 Medium Deep Survey for which we obtained spectroscopic host-galaxy redshifts. Our classifier achieves an overall accuracy of 82%, with completenesses and purities of >80% for the best classes (SNe Ia and superluminous SNe). For the worst performing SN class (SNe Ibc), the completeness and purity fall to 37% and 21%, respectively. Our classifier provides 1257 newly classified SNe Ia, 521 SNe II, 298 SNe Ibc, 181 SNe IIn, and 58 SLSNe. These are among the largest uniformly observed samples of SNe available in the literature and will enable a wide range of statistical studies of each class.

Updated to match published version in ApJ. Companion paper to Villar et al. (2020). Full machine-readable tables available as ancillary files at right

References in corpus (30)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• The Zwicky Transient Facility: System Overview, Performance, and First Results
• The Sixteenth Data Release of the Sloan Digital Sky Surveys: First Release from the APOGEE-2 Southern Survey and Full Release of eBOSS Spectra
• zCOSMOS: A Large VLT/VIMOS redshift survey covering 0 < z < 3 in the COSMOS field
• Improved Distances to Type Ia Supernovae with Multicolor Light Curve Shapes: MLCS2k2
• The type Ia supernova SNLS-03D3bb from a super-Chandrasekhar-mass white dwarf star
• An ultraviolet-optical flare from the tidal disruption of a helium-rich stellar core
• Rapidly-Evolving and Luminous Transients from Pan-STARRS1
• Towards Characterization of the Type IIP Supernova Progenitor Population: a Statistical Sample of Light Curves from Pan-STARRS1
• Deep ATLAS radio observations of the CDFS-SWIRE field
• Early-time light curves of Type Ib/c supernovae from the SDSS-II Supernova Survey
• The 2dF-SDSS LRG and QSO (2SLAQ) Luminous Red Galaxy Survey
• Calibration of the optical mass proxy for clusters of galaxies and an update of the WHL12 cluster catalog
• LOSS Revisited - II: The relative rates of different types of supernovae vary between low- and high-mass galaxies
• The Complete Calibration of the Color-Redshift Relation (C3R2) Survey: Analysis and Data Release 2
• Hydrogen-Poor Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey
• Physical properties of galaxies and their evolution in the VIMOS VLT Deep Survey. I. The evolution of the mass-metallicity relation up to z~0.9
• SuperRAENN: A Semi-supervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium Deep Survey Supernovae
• SNLS Spectroscopy: Testing for Evolution in Type Ia Supernovae
• OzDES multi-object fibre spectroscopy for the Dark Energy Survey: Results and second data release
• LOSS Revisited - I: Unraveling correlations between supernova rates and galaxy properties, as measured in a reanalysis of the Lick Observatory Supernova Search
• Photometric Redshifts with the LSST: Evaluating Survey Observing Strategies
• Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning
• Obscured and unobscured AGN populations in a hard-X-ray selected sample of the XMDS survey
• FLEET: A Redshift-Agnostic Machine Learning Pipeline to Rapidly Identify Hydrogen-Poor Superluminous Supernovae
• A Plethora of AGN Among Lyman Alpha Galaxies at Low Redshift
• Low redshift quasars in the SDSS Stripe 82. The local environments
• Luminous Red Galaxy Clustering at z~0.7 - First Results using AAOmega
• Real-Time Value-Driven Data Augmentation in the Era of LSST
• Star formation and morphological properties of galaxies in the Pan-STARRS $3\pi$ survey- I. A machine learning approach to galaxy and supernova classification

Cited by in corpus (14)

• The Young Supernova Experiment: Survey Goals, Overview, and Operations
• The DAWES review 10: The impact of deep learning for the analysis of galaxy surveys
• SuperRAENN: A Semi-supervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium Deep Survey Supernovae
• A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
• The Young Supernova Experiment Data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae
• Superluminous supernovae: an explosive decade
• Luminous Supernovae: Unveiling a Population Between Superluminous and Normal Core-collapse Supernovae
• SALT3-NIR: Taking the Open-Source Type Ia Supernova Model to Longer Wavelengths for Next-Generation Cosmological Measurements
• ParSNIP: Generative Models of Transient Light Curves with Physics-Enabled Deep Learning
• Identifying Tidal Disruption Events with an Expansion of the FLEET Machine Learning Algorithm
• Identification of Grand-design and Flocculent Spirals from SDSS using Convolutional Neural network
• Photometrically-Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine Learning-Based Classification
• AMPEL workflows for LSST: Modular and reproducible real-time photometric classification
• A Photometric Classifier for Tidal Disruption Events in Rubin LSST