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

Kessler, R and Brout, D and Desai, Shantanu and et al, . (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). ISSN 0035-8711

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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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IITH Creators:
IITH CreatorsORCiD
Desai, Shantanuhttp://orcid.org/0000-0002-0466-3288
Item Type: Article
Uncontrolled Keywords: (cosmology:) dark energy,cosmology,supernovae,techniques,Indexed in Scopus and WoS
Subjects: Physics
Divisions: Department of Physics
Depositing User: Library Staff
Date Deposited: 21 Oct 2019 10:09
Last Modified: 21 Oct 2019 10:09
URI: http://raiith.iith.ac.in/id/eprint/6703
Publisher URL: http://doi.org/10.1093/mnras/stz463
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