First cosmology results using supernovae ia from the dark energy survey: survey overview, performance, and supernova spectroscopy

Smith, M, D'Andrea, C B, Sullivan, M, Möller, A, Nichol, R C, Thomas, R C, Kim, A G, Sako, M, Castander, F J, Filippenko, A V, Foley, R J, Galbany, L, González-Gaitán, S, Kasai, E, Romer, A K and others, (2020) First cosmology results using supernovae ia from the dark energy survey: survey overview, performance, and supernova spectroscopy. Astronomical Journal, 160 (6). a267 1-21. ISSN 0004-6256

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Abstract

We present details on the observing strategy, data-processing techniques, and spectroscopic targeting algorithms for the first three years of operation for the Dark Energy Survey Supernova Program (DES-SN). This five-year program using the Dark Energy Camera mounted on the 4 m Blanco telescope in Chile was designed to discover and follow supernovae (SNe) Ia over a wide redshift range (0.05 < z < 1.2) to measure the equation-of-state parameter of dark energy. We describe the SN program in full: Strategy, observations, data reduction, spectroscopic follow-up observations, and classification. From three seasons of data, we have discovered 12,015 likely SNe, 308 of which have been spectroscopically confirmed, including 251 SNe Ia over a redshift range of 0.017 < z < 0.85. We determine the effective spectroscopic selection function for our sample and use it to investigate the redshiftdependent bias on the distance moduli of SNe Ia we have classified. The data presented here are used for the first cosmology analysis by DES-SN ("DES-SN3YR"), the results of which are given in Dark Energy Survey Collaboration et al. The 489 spectra that are used to define the DES-SN3YR sample are publicly available at https://des.ncsa.illinois.edu/releases/sn.

Item Type: Article
Schools and Departments: School of Mathematical and Physical Sciences > Physics and Astronomy
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 19 Jan 2021 09:01
Last Modified: 19 Jan 2021 11:00
URI: http://sro.sussex.ac.uk/id/eprint/96563

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