The Dark Energy Survey Supernova Program: Modelling selection efficiency and observed core collapse supernova contamination

TL;DR

This paper develops a realistic simulation framework for photometric supernova surveys, specifically for the Dark Energy Survey, to accurately model selection efficiency and core collapse supernova contamination, aiding cosmological analyses.

Contribution

It introduces a novel simulation framework that reproduces observed supernova and host galaxy properties without fine-tuning, improving bias correction in cosmological studies.

Findings

Simulations match observed distributions including Hubble residuals.

Estimated core collapse contamination in DES SN sample is around 7%.

Framework will be essential for bias correction in cosmological analyses.

Abstract

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-year 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 5.8 to 9.3 per cent, with an average of 7.0 per cent and 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.