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

Published

Journal Article

© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. 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.

Full Text

Duke Authors

Cited Authors

  • Kessler, R; Brout, D; D'Andrea, CB; Davis, TM; Hinton, SR; Kim, AG; Lasker, J; Lidman, C; MacAulay, E; Möller, A; Sako, M; Scolnic, D; Smith, M; Sullivan, M; Zhang, B; Andersen, P; Asorey, J; Avelino, A; Calcino, J; Carollo, D; Challis, P; Childress, M; Clocchiatti, A; Crawford, S; Filippenko, AV; Foley, RJ; Glazebrook, K; Hoormann, JK; Kasai, E; Kirshner, RP; Lewis, GF; Mandel, KS; March, M; Morganson, E; Muthukrishna, D; Nugent, P; Pan, YC; Sommer, NE; Swann, E; Thomas, RC; Tucker, BE; Uddin, SA; Abbott, TMC; Allam, S; Annis, J; Avila, S; Banerji, M; Bechtol, K; Bertin, E; Brooks, D; Buckley-Geer, E; Burke, DL; Carnero Rosell, A; Carrasco Kind, M; Carretero, J; Castander, FJ; Crocce, M; Da Costa, LN; Davis, C; De Vicente, J; Desai, S; Diehl, HT; Doel, P; Eifler, TF; Flaugher, B; Fosalba, P; Frieman, J; García-Bellido, J; Gaztanaga, E; Gerdes, DW; Gruen, D; Gruendl, RA; Gutierrez, G; Hartley, WG; Hollowood, DL; Honscheid, K; James, DJ; Johnson, MWG; Johnson, MD; Krause, E; Kuehn, K; Kuropatkin, N; Lahav, O; Li, TS; Lima, M; Marshall, JL; Martini, P; Menanteau, F; Miller, CJ; Miquel, R; Nord, B; Plazas, AA; Roodman, A; Sanchez, E; Scarpine, V; Schindler, R; Schubnell, M; Serrano, S; Sevilla-Noarbe, I; Soares-Santos, M

Published Date

  • May 1, 2019

Published In

Volume / Issue

  • 485 / 1

Start / End Page

  • 1171 - 1187

Electronic International Standard Serial Number (EISSN)

  • 1365-2966

International Standard Serial Number (ISSN)

  • 0035-8711

Digital Object Identifier (DOI)

  • 10.1093/mnras/stz463

Citation Source

  • Scopus