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arXiv · 1604.06138

Host Galaxy Identification for Supernova Surveys

Ravi R. Gupta·Steve Kuhlmann·Eve Kovacs·Harold Spinka·Richard Kessler·Daniel A. Goldstein·Camille Liotine·Katarzyna Pomian·Chris B. D'Andrea·Mark Sullivan·Jorge Carretero·Francisco J. Castander·Robert C. Nichol·David A. Finley·John A. Fischer·Ryan J. Foley·Alex G. Kim·Andreas Papadopoulos·Masao Sako·Daniel M. Scolnic·Mathew Smith·Brad E. Tucker·Syed Uddin·Rachel C. Wolf·Fang Yuan·Tim M. C. Abbott·Filipe B. Abdalla·Aurelien Benoit-Levy·Emmanuel Bertin·David Brooks·Aurelio Carnero Rosell·Matias Carrasco Kind·Carlos E. Cunha·Luiz N. da Costa·Shantanu Desai·Peter Doel·Tim F. Eifler·August E. Evrard·Brenna Flaugher·Pablo Fosalba·Enrique Gaztanaga·Daniel Gruen·Robert Gruendl·David J. James·Kyler Kuehn·Nikolay Kuropatkin·Marcio A. G. Maia·Jennifer L. Marshall·Ramon Miquel·Andres A. Plazas·A. Kathy Romer·Eusebio Sanchez·Michael Schubnell·Ignacio Sevilla-Noarbe·Flavia Sobreira·Eric Suchyta·Molly E. C. Swanson·Gregory Tarle·Alistair R. Walker·William Wester
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Abstract

Host galaxy identification is a crucial step for modern supernova (SN) surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope (LSST), which will discover SNe by the thousands. Spectroscopic resources are limited, 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.

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Ravi R. Gupta, Steve Kuhlmann, Eve Kovacs, Harold Spinka, Richard Kessler, Daniel A. Goldstein, Camille Liotine, Katarzyna Pomian, Chris B. D'Andrea, Mark Sullivan, Jorge Carretero, Francisco J. Castander, Robert C. Nichol, David A. Finley, John A. Fischer, Ryan J. Foley, Alex G. Kim, Andreas Papadopoulos, Masao Sako, Daniel M. Scolnic, Mathew Smith, Brad E. Tucker, Syed Uddin, Rachel C. Wolf, Fang Yuan, Tim M. C. Abbott, Filipe B. Abdalla, Aurelien Benoit-Levy, Emmanuel Bertin, David Brooks, Aurelio Carnero Rosell, Matias Carrasco Kind, Carlos E. Cunha, Luiz N. da Costa, Shantanu Desai, Peter Doel, Tim F. Eifler, August E. Evrard, Brenna Flaugher, Pablo Fosalba, Enrique Gaztanaga, Daniel Gruen, Robert Gruendl, David J. James, Kyler Kuehn, Nikolay Kuropatkin, Marcio A. G. Maia, Jennifer L. Marshall, Ramon Miquel, Andres A. Plazas, A. Kathy Romer, Eusebio Sanchez, Michael Schubnell, Ignacio Sevilla-Noarbe, Flavia Sobreira, Eric Suchyta, Molly E. C. Swanson, Gregory Tarle, Alistair R. Walker, William Wester. 2016-04-20. Host Galaxy Identification for Supernova Surveys. https://doi.org/10.3847/0004-6256/152/6/154

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