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First cosmology results using Type Ia supernova from the Dark Energy Survey: simulations to correct supernova distance biases

First cosmology results using Type Ia supernova from the Dark Energy Survey: simulations to correct supernova distance biases
First cosmology results using Type Ia supernova from the Dark Energy Survey: simulations to correct supernova distance biases
We describe catalogue-level simulations of Type Ia supernova (SN Ia) light curves in the Dark Energy Survey Supernova Program (DES-SN) and in low-redshift samples from the Center for Astrophysics (CfA) and the Carnegie Supernova Project (CSP). These simulations are used to model biases from selection effects and light-curve analysis and to determine bias corrections for SN Ia distance moduli that are used to measure cosmological parameters. To generate realistic light curves, the simulation uses a detailed SN Ia model, incorporates information from observations (point spread function, sky noise, zero-point), and uses summary information (e.g. detection efficiency versus signal-to-noise ratio) based on 10 000 fake SN light curves whose fluxes were overlaid on images and processed with our analysis pipelines. The quality of the simulation is illustrated by predicting distributions observed in the data. Averaging within redshift bins, we find distance modulus biases up to 0.05 mag over the redshift ranges of the low-z and DES-SN samples. For individual events, particularly those with extreme red or blue colour, distance biases can reach 0.4 mag. Therefore, accurately determining bias corrections is critical for precision measurements of cosmological parameters. Files used to make these corrections are available at https://des.ncsa.illinois.edu/releases/sn.
0035-8711
1171-1187
Sullivan, Mark
2f31f9fa-8e79-4b35-98e2-0cb38f503850
Smith, Mathew
8bdc74e1-a37b-434d-ae75-82763109bf7a
Childress, Michael
7d0e608c-b9de-4631-bab5-7a2b810a0a2b
Kessler, R.
73a1d852-9d13-408f-94c1-2bd3241d47e5
Brout, D.
2e6c15c6-38ee-4593-b3b6-3c3f406b4fb1
D’Andrea, C.B.
d1867567-d046-4c56-a443-415dfb5e7326
DES Collaboration
Sullivan, Mark
2f31f9fa-8e79-4b35-98e2-0cb38f503850
Smith, Mathew
8bdc74e1-a37b-434d-ae75-82763109bf7a
Childress, Michael
7d0e608c-b9de-4631-bab5-7a2b810a0a2b
Kessler, R.
73a1d852-9d13-408f-94c1-2bd3241d47e5
Brout, D.
2e6c15c6-38ee-4593-b3b6-3c3f406b4fb1
D’Andrea, C.B.
d1867567-d046-4c56-a443-415dfb5e7326

Sullivan, Mark, Smith, Mathew, Childress, Michael, Kessler, R., Brout, D. and D’Andrea, C.B. , DES Collaboration (2019) First cosmology results using Type Ia supernova from the Dark Energy Survey: simulations to correct supernova distance biases. Monthly Notices of the Royal Astronomical Society, 485 (1), 1171-1187. (doi:10.1093/mnras/stz463).

Record type: Article

Abstract

We describe catalogue-level simulations of Type Ia supernova (SN Ia) light curves in the Dark Energy Survey Supernova Program (DES-SN) and in low-redshift samples from the Center for Astrophysics (CfA) and the Carnegie Supernova Project (CSP). These simulations are used to model biases from selection effects and light-curve analysis and to determine bias corrections for SN Ia distance moduli that are used to measure cosmological parameters. To generate realistic light curves, the simulation uses a detailed SN Ia model, incorporates information from observations (point spread function, sky noise, zero-point), and uses summary information (e.g. detection efficiency versus signal-to-noise ratio) based on 10 000 fake SN light curves whose fluxes were overlaid on images and processed with our analysis pipelines. The quality of the simulation is illustrated by predicting distributions observed in the data. Averaging within redshift bins, we find distance modulus biases up to 0.05 mag over the redshift ranges of the low-z and DES-SN samples. For individual events, particularly those with extreme red or blue colour, distance biases can reach 0.4 mag. Therefore, accurately determining bias corrections is critical for precision measurements of cosmological parameters. Files used to make these corrections are available at https://des.ncsa.illinois.edu/releases/sn.

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1811.02379 - Accepted Manuscript
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Accepted/In Press date: 5 February 2019
e-pub ahead of print date: 19 February 2019
Published date: May 2019

Identifiers

Local EPrints ID: 429668
URI: https://eprints.soton.ac.uk/id/eprint/429668
ISSN: 0035-8711
PURE UUID: 9456b0be-8398-495a-b069-aecd5d7aee7b
ORCID for Mark Sullivan: ORCID iD orcid.org/0000-0001-9053-4820
ORCID for Mathew Smith: ORCID iD orcid.org/0000-0002-3321-1432

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Date deposited: 03 Apr 2019 16:30
Last modified: 12 Nov 2019 01:38

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