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

Abstract

We describe catalog-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 (PSF, sky noise, zero point), and uses summary information (e.g., detection efficiency vs. 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 color, 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.

First cosmology results using type Ia supernovae from the Dark Energy Survey: the effect of host galaxy properties on supernova luminosity

“…Simulations of the DES-SN sample are performed using the 'SuperNova ANAlysis' (snana) software package (Kessler et al 2009). The simulation inputs include a rest-frame SALT-II SED model (Guy et al 2010;Betoule et al 2014), an intrinsic scatter model (Guy et al 2010;Kessler et al 2013), SNIa population parameters (Scolnic & Kessler 2016), the volumetric rate of SNIa and its evolution with redshift (Perrett et al 2010), a library of survey observations with measured observing parameters (sky noise, PSF, zero point), DECam filter transmission curves and a model of the DES detection and spectroscopic follow-up efficiency (Kessler et al 2019, Figure 4). The application of the snana methodology to simulating the DES3YR sample is described in detail in Kessler et al (2019), while a detailed analysis of the effect that systematic uncertainties have on the resulting cosmological constraints is given in B19.…”

Machine learning and the future of supernova cosmology

“…Once this is achieved however, it will be important to keep in mind that this is still only part of the difficult path towards fully photometric SN cosmology. The final pipeline will also need to take into account the presence of g https://antares.noao.edu h http://alerce.science/ i https://fink-broker.readthedocs.io/en/latest/ probabilistic classifications in cosmological parameter estimation 9 and the proper determination of distance bias 25 given different strategies for selection of spectroscopic samples. There is certainly a long way to go but the recent developments within the astronomical community, as well as the high level of engagement of scientists from other research fields, show there is a huge potential for development on multiple fronts.…”

Cosmological Constraints from Multiple Probes in the Dark Energy Survey

“…Type Ia supernovae: The DES-SN sample is comprised of 207 spectroscopically confirmed SNe Ia in the redshift range 0.07 < z < 0.85. The sample-building and analysis pipelines are discussed in a series of papers that detail the SN Ia search and discovery [36,38,39], spectroscopic follow-up [56], photometry [57], calibration [58,59], simulations [60], and technique of accounting for selection bias [61,62]. The analysis methodology and systematic uncertainties are presented in Ref.…”