First cosmology results using Type Ia supernova from the Dark Energy Survey

simulations to correct supernova distance biases

R. Kessler*, D. Brout, C. B. D'Andrea, T. M. Davis, S. R. Hinton, A. G. Kim, J. Lasker, C. Lidman, E. MacAulay, A. Möller, M. Sako, D. Scolnic, M. Smith, M. Sullivan, B. Zhang, P. Andersen, J. Asorey, A. Avelino, J. Calcino, D. Carollo & 86 others P. Challis, M. Childress, A. Clocchiatti, S. Crawford, A. V. Filippenko, R. J. Foley, K. Glazebrook, J. K. Hoormann, E. Kasai, R. P. Kirshner, G. F. Lewis, K. S. Mandel, M. March, E. Morganson, D. Muthukrishna, P. Nugent, Y. C. Pan, N. E. Sommer, E. Swann, R. C. Thomas, B. E. Tucker, S. A. Uddin, T. M.C. Abbott, S. Allam, J. Annis, S. Avila, M. Banerji, K. Bechtol, E. Bertin, D. Brooks, E. Buckley-Geer, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, F. J. Castander, M. Crocce, L. N. da Costa, C. Davis, J. De Vicente, S. Desai, H. T. Diehl, P. Doel, T. F. Eifler, B. Flaugher, P. Fosalba, J. Frieman, J. García-Bellido, E. Gaztanaga, D. W. Gerdes, D. Gruen, R. A. Gruendl, G. Gutierrez, W. G. Hartley, D. L. Hollowood, K. Honscheid, D. J. James, M. W.G. Johnson, M. D. Johnson, E. Krause, K. Kuehn, N. Kuropatkin, O. Lahav, T. S. Li, M. Lima, J. L. Marshall, P. Martini, F. Menanteau, C. J. Miller, R. Miquel, B. Nord, A. A. Plazas, A. Roodman, E. Sanchez, V. Scarpine, R. Schindler, M. Schubnell, S. Serrano, I. Sevilla-Noarbe, M. Soares-Santos, F. Sobreira, E. Suchyta, G. Tarle, D. Thomas, A. R. Walker, Y. Zhang

*Corresponding author for this work

Research output: Contribution to journalArticle

9 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1171-1187
Number of pages17
JournalMonthly Notices of the Royal Astronomical Society
Volume485
Issue number1
DOIs
Publication statusPublished - 1 May 2019

    Fingerprint

Keywords

  • (cosmology:) dark energy
  • cosmology
  • supernovae
  • techniques

Cite this