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Title: Classifying supernovae using only galaxy data

We present a new method for probabilistically classifying supernovae (SNe) without using SN spectral or photometric data. Unlike all previous studies to classify SNe without spectra, this technique does not use any SN photometry. Instead, the method relies on host-galaxy data. We build upon the well-known correlations between SN classes and host-galaxy properties, specifically that core-collapse SNe rarely occur in red, luminous, or early-type galaxies. Using the nearly spectroscopically complete Lick Observatory Supernova Search sample of SNe, we determine SN fractions as a function of host-galaxy properties. Using these data as inputs, we construct a Bayesian method for determining the probability that an SN is of a particular class. This method improves a common classification figure of merit by a factor of >2, comparable to the best light-curve classification techniques. Of the galaxy properties examined, morphology provides the most discriminating information. We further validate this method using SN samples from the Sloan Digital Sky Survey and the Palomar Transient Factory. We demonstrate that this method has wide-ranging applications, including separating different subclasses of SNe and determining the probability that an SN is of a particular class before photometry or even spectra can. Since this method uses completely independent data frommore » light-curve techniques, there is potential to further improve the overall purity and completeness of SN samples and to test systematic biases of the light-curve techniques. Further enhancements to the host-galaxy method, including additional host-galaxy properties, combination with light-curve methods, and hybrid methods, should further improve the quality of SN samples from past, current, and future transient surveys.« less
Authors:
 [1] ;  [2]
  1. Astronomy Department, University of Illinois at Urbana-Champaign, 1002 West Green Street, Urbana, IL 61801 (United States)
  2. Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States)
Publication Date:
OSTI Identifier:
22341897
Resource Type:
Journal Article
Resource Relation:
Journal Name: Astrophysical Journal; Journal Volume: 778; Journal Issue: 2; Other Information: Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; CLASSIFICATION; COMPARATIVE EVALUATIONS; CORRELATIONS; GALAXIES; IMPURITIES; PHOTOMETRY; PROBABILITY; SKY; SPECTRA; SUPERNOVAE; TRANSIENTS; VISIBLE RADIATION