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The Dark Energy Survey supernova programme: modelling selection efficiency and observed core-collapse supernova contamination

The Dark Energy Survey supernova programme: modelling selection efficiency and observed core-collapse supernova contamination
The Dark Energy Survey supernova programme: modelling selection efficiency and observed core-collapse supernova contamination
The analysis of current and future cosmological surveys of Type Ia supernovae (SNe Ia) at high redshift depends on the accurate photometric classification of the SN events detected. Generating realistic simulations of photometric SN surveys constitutes an essential step for training and testing photometric classification algorithms, and for correcting biases introduced by selection effects and contamination arising from core-collapse SNe in the photometric SN Ia samples. We use published SN time-series spectrophotometric templates, rates, luminosity functions, and empirical relationships between SNe and their host galaxies to construct a framework for simulating photometric SN surveys. We present this framework in the context of the Dark Energy Survey (DES) 5-yr photometric SN sample, comparing our simulations of DES with the observed DES transient populations. We demonstrate excellent agreement in many distributions, including Hubble residuals, between our simulations and data. We estimate the core collapse fraction expected in the DES SN sample after selection requirements are applied and before photometric classification. After testing different modelling choices and astrophysical assumptions underlying our simulation, we find that the predicted contamination varies from 7.2 to 11.7 per cent, with an average of 8.8 per cent and an r.m.s. of 1.1 per cent. Our simulations are the first to reproduce the observed photometric SN and host galaxy properties in high-redshift surveys without fine-tuning the input parameters. The simulation methods presented here will be a critical component of the cosmology analysis of the DES photometric SN Ia sample: correcting for biases arising from contamination, and evaluating the associated systematic uncertainty.
surveys, supernovae: general, cosmology: observations
1365-2966
2819-2839
Vincenzi, M.
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Sullivan, M.
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Graur, O.
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Brout, D.
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Frohmaier, C.
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Costanzi, M.
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da Costa, L. N.
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Pereira, M. E. S.
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Desai, S.
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Diehl, H. T.
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Everett, S.
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Fosalba, P.
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Frieman, J.
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García-Bellido, J.
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Gutierrez, G.
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Hollowood, D. L.
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The Dark Energy Survey Collaboration
Vincenzi, M.
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Sullivan, M.
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Frohmaier, C.
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Galbany, L.
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Hinton, S. R.
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Carnero Rosell, A.
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Carrasco Kind, M.
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Carretero, J.
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Castander, F. J.
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Choi, A.
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Costanzi, M.
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da Costa, L. N.
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Pereira, M. E. S.
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De Vicente, J.
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Desai, S.
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Doel, P.
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Everett, S.
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Ferrero, I.
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Fosalba, P.
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Frieman, J.
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García-Bellido, J.
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Gutierrez, G.
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Hollowood, D. L.
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The Dark Energy Survey Collaboration (2021) The Dark Energy Survey supernova programme: modelling selection efficiency and observed core-collapse supernova contamination. Monthly Notices of the Royal Astronomical Society, 505 (2), 2819-2839. (doi:10.1093/mnras/stab1353).

Record type: Article

Abstract

The analysis of current and future cosmological surveys of Type Ia supernovae (SNe Ia) at high redshift depends on the accurate photometric classification of the SN events detected. Generating realistic simulations of photometric SN surveys constitutes an essential step for training and testing photometric classification algorithms, and for correcting biases introduced by selection effects and contamination arising from core-collapse SNe in the photometric SN Ia samples. We use published SN time-series spectrophotometric templates, rates, luminosity functions, and empirical relationships between SNe and their host galaxies to construct a framework for simulating photometric SN surveys. We present this framework in the context of the Dark Energy Survey (DES) 5-yr photometric SN sample, comparing our simulations of DES with the observed DES transient populations. We demonstrate excellent agreement in many distributions, including Hubble residuals, between our simulations and data. We estimate the core collapse fraction expected in the DES SN sample after selection requirements are applied and before photometric classification. After testing different modelling choices and astrophysical assumptions underlying our simulation, we find that the predicted contamination varies from 7.2 to 11.7 per cent, with an average of 8.8 per cent and an r.m.s. of 1.1 per cent. Our simulations are the first to reproduce the observed photometric SN and host galaxy properties in high-redshift surveys without fine-tuning the input parameters. The simulation methods presented here will be a critical component of the cosmology analysis of the DES photometric SN Ia sample: correcting for biases arising from contamination, and evaluating the associated systematic uncertainty.

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Accepted/In Press date: 23 April 2021
e-pub ahead of print date: 26 May 2021
Published date: 1 August 2021
Keywords: surveys, supernovae: general, cosmology: observations

Identifiers

Local EPrints ID: 457728
URI: http://eprints.soton.ac.uk/id/eprint/457728
ISSN: 1365-2966
PURE UUID: d954f695-d914-422e-a7e8-503cc21b4173
ORCID for M. Sullivan: ORCID iD orcid.org/0000-0001-9053-4820
ORCID for P. Wiseman: ORCID iD orcid.org/0000-0002-3073-1512

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Date deposited: 16 Jun 2022 00:26
Last modified: 28 Feb 2024 03:01

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Contributors

Author: M. Vincenzi
Author: M. Sullivan ORCID iD
Author: O. Graur
Author: D. Brout
Author: C. Frohmaier
Author: L. Galbany
Author: C. P. Gutiérrez
Author: S. R. Hinton
Author: L. Kelsey
Author: R. Kessler
Author: E. Kovacs
Author: S. Kuhlmann
Author: J. Lasker
Author: C. Lidman
Author: A. Möller
Author: R. C. Nichol
Author: M. Sako
Author: D. Scolnic
Author: E. Swann
Author: P. Wiseman ORCID iD
Author: J. Asorey
Author: B. E. Tucker
Author: M. Aguena
Author: S. Allam
Author: S. Avila
Author: E. Bertin
Author: D. L. Burke
Author: A. Carnero Rosell
Author: M. Carrasco Kind
Author: J. Carretero
Author: F. J. Castander
Author: A. Choi
Author: M. Costanzi
Author: L. N. da Costa
Author: M. E. S. Pereira
Author: J. De Vicente
Author: S. Desai
Author: H. T. Diehl
Author: P. Doel
Author: S. Everett
Author: I. Ferrero
Author: P. Fosalba
Author: J. Frieman
Author: J. García-Bellido
Author: E. Gaztanaga
Author: D. W. Gerdes
Author: D. Gruen
Author: R. A. Gruendl
Author: G. Gutierrez
Author: D. L. Hollowood
Corporate Author: The Dark Energy Survey Collaboration

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