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Host Galaxy Identification for Supernova Surveys

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Host Galaxy Identification for Supernova Surveys. / Gupta, Ravi R.; Kuhlmann, Steve; Kovacs, Eve et al.
In: The Astronomical Journal, Vol. 152, No. 6, 154, 10.12.2016.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Gupta, RR, Kuhlmann, S, Kovacs, E, Spinka, H, Kessler, R, Goldstein, DA, Liotine, C, Pomian, K, D'Andrea, CB, Sullivan, M, Carretero, J, Castander, FJ, Nichol, RC, Finley, DA, Fischer, JA, Foley, RJ, Kim, AG, Papadopoulos, A, Sako, M, Scolnic, DM, Smith, M, Tucker, BE, Uddin, S, Wolf, RC, Yuan, F, Abbott, TMC, Abdalla, FB, Benoit-Lévy, A, Bertin, E, Brooks, D, Carnero Rosell, A, Carrasco Kind, M, Cunha, CE, da Costa, LN, Desai, S, Doel, P, Eifler, TF, Evrard, AE, Flaugher, B, Fosalba, P, Gaztañaga, E, Gruen, D, Gruendl, R, James, DJ, Kuehn, K, Kuropatkin, N, Maia, MAG, Marshall, JL, Miquel, R & Plazas, AA 2016, 'Host Galaxy Identification for Supernova Surveys', The Astronomical Journal, vol. 152, no. 6, 154. https://doi.org/10.3847/0004-6256/152/6/154

APA

Gupta, R. R., Kuhlmann, S., Kovacs, E., Spinka, H., Kessler, R., Goldstein, D. A., Liotine, C., Pomian, K., D'Andrea, C. B., Sullivan, M., Carretero, J., Castander, F. J., Nichol, R. C., Finley, D. A., Fischer, J. A., Foley, R. J., Kim, A. G., Papadopoulos, A., Sako, M., ... Plazas, A. A. (2016). Host Galaxy Identification for Supernova Surveys. The Astronomical Journal, 152(6), Article 154. https://doi.org/10.3847/0004-6256/152/6/154

Vancouver

Gupta RR, Kuhlmann S, Kovacs E, Spinka H, Kessler R, Goldstein DA et al. Host Galaxy Identification for Supernova Surveys. The Astronomical Journal. 2016 Dec 10;152(6):154. doi: 10.3847/0004-6256/152/6/154

Author

Gupta, Ravi R. ; Kuhlmann, Steve ; Kovacs, Eve et al. / Host Galaxy Identification for Supernova Surveys. In: The Astronomical Journal. 2016 ; Vol. 152, No. 6.

Bibtex

@article{d2e6ad9747fe4ae3855924d131f45863,
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 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.",
author = "Gupta, {Ravi R.} and Steve Kuhlmann and Eve Kovacs and Harold Spinka and Richard Kessler and Goldstein, {Daniel A.} and Camille Liotine and Katarzyna Pomian and D'Andrea, {Chris B.} and Mark Sullivan and Jorge Carretero and Castander, {Francisco J.} and Nichol, {Robert C.} and Finley, {David A.} and Fischer, {John A.} and Foley, {Ryan J.} and Kim, {Alex G.} and Andreas Papadopoulos and Masao Sako and Scolnic, {Daniel M.} and Mathew Smith and Tucker, {Brad E.} and Syed Uddin and Wolf, {Rachel C.} and Fang Yuan and Abbott, {Tim M. C.} and Abdalla, {Filipe B.} and Aur{\'e}lien Benoit-L{\'e}vy and Emmanuel Bertin and David Brooks and {Carnero Rosell}, Aurelio and {Carrasco Kind}, Matias and Cunha, {Carlos E.} and {da Costa}, {Luiz N.} and Shantanu Desai and Peter Doel and Eifler, {Tim F.} and Evrard, {August E.} and Brenna Flaugher and Pablo Fosalba and Enrique Gazta{\~n}aga and Daniel Gruen and Robert Gruendl and James, {David J.} and Kyler Kuehn and Nikolay Kuropatkin and Maia, {Marcio A. G.} and Marshall, {Jennifer L.} and Ramon Miquel and Plazas, {Andr{\'e}s A.}",
year = "2016",
month = dec,
day = "10",
doi = "10.3847/0004-6256/152/6/154",
language = "English",
volume = "152",
journal = "The Astronomical Journal",
issn = "0004-6256",
publisher = "IOP Publishing Ltd.",
number = "6",

}

RIS

TY - JOUR

T1 - Host Galaxy Identification for Supernova Surveys

AU - Gupta, Ravi R.

AU - Kuhlmann, Steve

AU - Kovacs, Eve

AU - Spinka, Harold

AU - Kessler, Richard

AU - Goldstein, Daniel A.

AU - Liotine, Camille

AU - Pomian, Katarzyna

AU - D'Andrea, Chris B.

AU - Sullivan, Mark

AU - Carretero, Jorge

AU - Castander, Francisco J.

AU - Nichol, Robert C.

AU - Finley, David A.

AU - Fischer, John A.

AU - Foley, Ryan J.

AU - Kim, Alex G.

AU - Papadopoulos, Andreas

AU - Sako, Masao

AU - Scolnic, Daniel M.

AU - Smith, Mathew

AU - Tucker, Brad E.

AU - Uddin, Syed

AU - Wolf, Rachel C.

AU - Yuan, Fang

AU - Abbott, Tim M. C.

AU - Abdalla, Filipe B.

AU - Benoit-Lévy, Aurélien

AU - Bertin, Emmanuel

AU - Brooks, David

AU - Carnero Rosell, Aurelio

AU - Carrasco Kind, Matias

AU - Cunha, Carlos E.

AU - da Costa, Luiz N.

AU - Desai, Shantanu

AU - Doel, Peter

AU - Eifler, Tim F.

AU - Evrard, August E.

AU - Flaugher, Brenna

AU - Fosalba, Pablo

AU - Gaztañaga, Enrique

AU - Gruen, Daniel

AU - Gruendl, Robert

AU - James, David J.

AU - Kuehn, Kyler

AU - Kuropatkin, Nikolay

AU - Maia, Marcio A. G.

AU - Marshall, Jennifer L.

AU - Miquel, Ramon

AU - Plazas, Andrés A.

PY - 2016/12/10

Y1 - 2016/12/10

N2 - 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.

AB - 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.

UR - https://ui.adsabs.harvard.edu/#abs/2016AJ....152..154G

U2 - 10.3847/0004-6256/152/6/154

DO - 10.3847/0004-6256/152/6/154

M3 - Journal article

VL - 152

JO - The Astronomical Journal

JF - The Astronomical Journal

SN - 0004-6256

IS - 6

M1 - 154

ER -