SUPERNOVA SIMULATIONS AND STRATEGIES FOR THE DARK ENERGY SURVEY

By J. P. Bernstein, R. Kessler, S. Kuhlmann, R. Biswas, E. Kovacs, G. Aldering, I. Crane, C. B. D'Andrea, D. Finley, J. Frieman and 12 more. 2011

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We present an analysis of supernova light curves simulated for the upcoming Dark Energy Survey (DES) supernova search. The simulations employ a code suite that generates and fits realistic light curves in order to obtain distance modulus/redshift pairs that are passed to a cosmology fitter. We investigated several different survey strategies including field selection, supernova selection biases, and photometric redshift measurements. Using the results of this study, we chose a 30 deg2 search area in the griz filter set. We forecast (1) that this survey will provide a homogeneous sample of up to 4000 Type Ia supernovae in the redshift range 0.05 <z < 1.2 and (2) that the increased red efficiency of the DES camera will significantly improve high-redshift color measurements. The redshift of each supernova with an identified host galaxy will be obtained from spectroscopic observations of the host. A supernova spectrum will be obtained for a subset of the sample, which will be utilized for control studies. In addition, we have investigated the use of combined photometric redshifts taking into account data from both the host and supernova. We have investigated and estimated the likely contamination from core-collapse supernovae based on photometric identification, and have found that a Type Ia supernova sample purity of up to 98% is obtainable given specific assumptions. Furthermore, we present systematic uncertainties due to sample purity, photometric calibration, dust extinction priors, filter-centroid shifts, and inter-calibration. We conclude by estimating the uncertainty on the cosmological parameters that will be measured from the DES supernova data.

Subfields: Astronomy and Astrophysics, Electrical and Electronic Engineering, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/0cde29d602fde9707c6aa7e4c44e80951c680d3e

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Citations in the corpus

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING (2016, methodology): “The current commonly used dataset, the Joint Light curve Analysis (JLA) (Betoule et al. 2014), contains only 740 supernovae, while DES is expected to detect thousands (Bernstein et al. 2012) and LSST hundreds of thousands (LSST Science Collaboration 2009) of supernovae.”
Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples (2014, background): “In particular, the Dark Energy Survey (DES) experiment, which has just begun its high-redshift SNe survey (Bernstein et al. 2012), will exploit improved sensitivity in the infrared to reduce the model-calibration-cosmology degeneracy.”
Reconstruction of dark energy and expansion dynamics using Gaussian processes (2012, methodology): “The Dark Energy Survey (DES) - Supernova Survey [33] is expected to obtain high quality light-curves for about 4000 SNe Ia up to redshift z = 1.”
The VISTA deep extragalactic observations (VIDEO) survey (2012, background): “The VIDEO fields are key fields within the Dark Energy Survey (DES1) SN survey (Bernstein et al. 2012) and will be able to provide near-IR data points on the light curves of SNe discovered in the DES SN survey.”
Observational probes of cosmic acceleration (2012, background): “, the half-light radius) to parameters that are magnification-independent and can be measured in photometric surveys (Bertin and Lombardi, 2006), such as the surface brightness, the Sersić index, or (for AGN) variability amplitude.”
Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples (2016, methodology): “These SN programs include the recently completed Pan-STARRS1 (Kaiser et al. 2002), the ongoing Dark Energy Survey Supernova Program (DES-SN: Bernstein et al. 2012), and the Large Synoptic Survey Telescope (LSST: Ivezic et al. 2008; LSST Science Collaboration et al. 2009), expected to begin in the…”
The Data Release of the Sloan Digital Sky Survey-II Supernova Survey (2014, methodology): “The SDSS work on photometric identification represents an important example analysis for ongoing and future large surveys, such as PanSTARRS (Scolnic et al. 2013a; Rest et al. 2013), DES (Bernstein et al. 2012) and LSST (Tyson 2002), where full spectroscopic follow up of all active SN candidates wil”
THE DIFFERENCE IMAGING PIPELINE FOR THE TRANSIENT SEARCH IN THE DARK ENERGY SURVEY (2015, methodology): “While the search strategy was optimized to find SNe Ia to build a Hubble diagram for measuring dark energy properties, (Bernstein et al. 2012), DiffImg does not depend on the transient type.”