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Title: HOST GALAXY IDENTIFICATION FOR SUPERNOVA SURVEYS

Abstract

Host galaxy identification is a crucial step for modern supernova (SN) surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope, which will discover SNe by the thousands. Spectroscopic resources are limited, and so in the absence of real-time SN spectra these surveys must rely on host galaxy spectra to obtain accurate redshifts for the Hubble diagram and to improve photometric classification of SNe. In addition, SN luminosities are known to correlate with host-galaxy properties. Therefore, reliable identification of host galaxies is essential for cosmology and SN science. We simulate SN events and their locations within their host galaxies to develop and test methods for matching SNe to their hosts. We use both real and simulated galaxy catalog data from the Advanced Camera for Surveys General Catalog and MICECATv2.0, respectively. We also incorporate “hostless” SNe residing in undetected faint hosts into our analysis, with an assumed hostless rate of 5%. Our fully automated algorithm is run on catalog data and matches SNe to their hosts with 91% accuracy. We find that including a machine learning component, run after the initial matching algorithm, improves the accuracy (purity) of the matching to 97% with a 2% cost in efficiencymore » (true positive rate). Although the exact results are dependent on the details of the survey and the galaxy catalogs used, the method of identifying host galaxies we outline here can be applied to any transient survey.« less

Authors:
; ; ; ; ;  [1]; ;  [2];  [3]; ; ;  [4];  [5]; ;  [6];  [7]; ;  [8];  [9];
  1. Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439 (United States)
  2. Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637 (United States)
  3. Department of Astronomy, University of California, Berkeley, 501 Campbell Hall #3411, Berkeley, CA 94720 (United States)
  4. Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth, PO1 3FX (United Kingdom)
  5. Department of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ (United Kingdom)
  6. Institut de Ciències de l’Espai, IEEC-CSIC, Campus UAB, Carrer de Can Magrans, s/n, E-08193 Bellaterra, Barcelona (Spain)
  7. Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510 (United States)
  8. Department of Physics and Astronomy, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA 19104 (United States)
  9. Department of Astronomy, University of Illinois, 1002 W. Green Street, Urbana, IL 61801 (United States)
Publication Date:
OSTI Identifier:
22662933
Resource Type:
Journal Article
Journal Name:
Astronomical Journal (Online)
Additional Journal Information:
Journal Volume: 152; Journal Issue: 6; Other Information: Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 1538-3881
Country of Publication:
United States
Language:
English
Subject:
79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; ACCURACY; ALGORITHMS; CAMERAS; CLASSIFICATION; COSMOLOGY; DIAGRAMS; GALAXIES; LUMINOSITY; NONLUMINOUS MATTER; RED SHIFT; SUPERNOVAE; TELESCOPES

Citation Formats

Gupta, Ravi R., Kuhlmann, Steve, Kovacs, Eve, Spinka, Harold, Liotine, Camille, Pomian, Katarzyna, Kessler, Richard, Scolnic, Daniel M., Goldstein, Daniel A., D’Andrea, Chris B., Nichol, Robert C., Papadopoulos, Andreas, Sullivan, Mark, Carretero, Jorge, Castander, Francisco J., Finley, David A., Fischer, John A., Sako, Masao, Foley, Ryan J., Kim, Alex G., E-mail: raviryan@gmail.com, and others, and. HOST GALAXY IDENTIFICATION FOR SUPERNOVA SURVEYS. United States: N. p., 2016. Web. doi:10.3847/0004-6256/152/6/154.
Gupta, Ravi R., Kuhlmann, Steve, Kovacs, Eve, Spinka, Harold, Liotine, Camille, Pomian, Katarzyna, Kessler, Richard, Scolnic, Daniel M., Goldstein, Daniel A., D’Andrea, Chris B., Nichol, Robert C., Papadopoulos, Andreas, Sullivan, Mark, Carretero, Jorge, Castander, Francisco J., Finley, David A., Fischer, John A., Sako, Masao, Foley, Ryan J., Kim, Alex G., E-mail: raviryan@gmail.com, & others, and. HOST GALAXY IDENTIFICATION FOR SUPERNOVA SURVEYS. United States. doi:10.3847/0004-6256/152/6/154.
Gupta, Ravi R., Kuhlmann, Steve, Kovacs, Eve, Spinka, Harold, Liotine, Camille, Pomian, Katarzyna, Kessler, Richard, Scolnic, Daniel M., Goldstein, Daniel A., D’Andrea, Chris B., Nichol, Robert C., Papadopoulos, Andreas, Sullivan, Mark, Carretero, Jorge, Castander, Francisco J., Finley, David A., Fischer, John A., Sako, Masao, Foley, Ryan J., Kim, Alex G., E-mail: raviryan@gmail.com, and others, and. Thu . "HOST GALAXY IDENTIFICATION FOR SUPERNOVA SURVEYS". United States. doi:10.3847/0004-6256/152/6/154.
@article{osti_22662933,
title = {HOST GALAXY IDENTIFICATION FOR SUPERNOVA SURVEYS},
author = {Gupta, Ravi R. and Kuhlmann, Steve and Kovacs, Eve and Spinka, Harold and Liotine, Camille and Pomian, Katarzyna and Kessler, Richard and Scolnic, Daniel M. and Goldstein, Daniel A. and D’Andrea, Chris B. and Nichol, Robert C. and Papadopoulos, Andreas and Sullivan, Mark and Carretero, Jorge and Castander, Francisco J. and Finley, David A. and Fischer, John A. and Sako, Masao and Foley, Ryan J. and Kim, Alex G., E-mail: raviryan@gmail.com and others, and},
abstractNote = {Host galaxy identification is a crucial step for modern supernova (SN) surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope, which will discover SNe by the thousands. Spectroscopic resources are limited, and so in the absence of real-time SN spectra these surveys must rely on host galaxy spectra to obtain accurate redshifts for the Hubble diagram and to improve photometric classification of SNe. In addition, SN luminosities are known to correlate with host-galaxy properties. Therefore, reliable identification of host galaxies is essential for cosmology and SN science. We simulate SN events and their locations within their host galaxies to develop and test methods for matching SNe to their hosts. We use both real and simulated galaxy catalog data from the Advanced Camera for Surveys General Catalog and MICECATv2.0, respectively. We also incorporate “hostless” SNe residing in undetected faint hosts into our analysis, with an assumed hostless rate of 5%. Our fully automated algorithm is run on catalog data and matches SNe to their hosts with 91% accuracy. We find that including a machine learning component, run after the initial matching algorithm, improves the accuracy (purity) of the matching to 97% with a 2% cost in efficiency (true positive rate). Although the exact results are dependent on the details of the survey and the galaxy catalogs used, the method of identifying host galaxies we outline here can be applied to any transient survey.},
doi = {10.3847/0004-6256/152/6/154},
journal = {Astronomical Journal (Online)},
issn = {1538-3881},
number = 6,
volume = 152,
place = {United States},
year = {2016},
month = {12}
}

Works referencing / citing this record:

Dependence of Type Ia supernova luminosities on their local environment
journal, July 2018