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The analysis of current and future cosmological surveys of Type Ia supernovae (SNe Ia) at high redshift depends on the accurate photometric classification of the SN events detected. Generating realistic simulations of photometric SN surveys constitutes an essential step for training and testing photometric classification algorithms, and for correcting biases introduced by selection effects and contamination arising from core-collapse SNe in the photometric SN Ia samples. We use published SN time-series spectrophotometric templates, rates, luminosity functions, and empirical relationships between SNe and their host galaxies to construct a framework for simulating photometric SN surveys. We present this framework in the context of the Dark Energy Survey (DES) 5-yr photometric SN sample, comparing our simulations of DES with the observed DES transient populations. We demonstrate excellent agreement in many distributions, including Hubble residuals, between our simulations and data. We estimate the core collapse fraction expected in the DES SN sample after selection requirements are applied and before photometric classification. After testing different modelling choices and astrophysical assumptions underlying our simulation, we find that the predicted contamination varies from 7.2 to 11.7 per cent, with an average of 8.8 per cent and an r.m.s. of 1.1 per cent. Our simulations are the first to reproduce the observed photometric SN and host galaxy properties in high-redshift surveys without fine-tuning the input parameters. The simulation methods presented here will be a critical component of the cosmology analysis of the DES photometric SN Ia sample: correcting for biases arising from contamination, and evaluating the associated systematic uncertainty. [ABSTRACT FROM AUTHOR]

We present a revised Spectral Adaptive Light Curve Template (SALT2) surface (SALT2-2021) for fitting the light curves of Type Ia supernovae (SNe Ia), which incorporates new measurements of zero-point calibration offsets and Milky Way extinction. The most notable change in the new surface occurs in the UV region. This new surface alters the distance measurements of SNe Ia, which can be used to investigate the nature of dark energy by probing the expansion history of the Universe. Using the revised SALT2 surface on public data from the first 3 yr of the Dark Energy Survey Supernova Program (combined with an external low- z SNe Ia sample) and combining with cosmic microwave background constraints, we find a change in the dark energy equation-of-state parameter, Δ w = 0.015 ± 0.004. This result highlights the continued importance of controlling and reducing systematic uncertainties, particularly with the next generation of supernova analyses aiming to improve constraints on dark energy properties. [ABSTRACT FROM AUTHOR]

Calibration uncertainties have been the leading systematic uncertainty in recent analyses using Type Ia supernovae (SNe Ia) to measure cosmological parameters. To improve the calibration, we present the application of spectral energy distribution-dependent 'chromatic corrections' to the SN light-curve photometry from the Dark Energy Survey (DES). These corrections depend on the combined atmospheric and instrumental transmission function for each exposure, and they affect photometry at the 0.01 mag (1 per cent) level, comparable to systematic uncertainties in calibration and photometry. Fitting our combined DES and low- z SN Ia sample with baryon acoustic oscillation (BAO) and cosmic microwave background (CMB) priors for the cosmological parameters Ωm (the fraction of the critical density of the universe comprised of matter) and w (the dark energy equation of state parameter), we compare those parameters before and after applying the corrections. We find the change in w and Ωm due to not including chromatic corrections is −0.002 and 0.000, respectively, for the DES-SN3YR sample with BAO and CMB priors, consistent with a larger DES-SN3YR-like simulation, which has a w -change of 0.0005 with an uncertainty of 0.008 and an Ωm change of 0.000 with an uncertainty of 0.002. However, when considering samples on individual CCDs we find large redshift-dependent biases (∼0.02 in distance modulus) for SN distances. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]