Dark Energy Survey Supernovae: Simulations and Survey Strategy

TL;DR

This paper introduces a simulation framework for the Dark Energy Survey supernova observations, incorporating realistic conditions and models to optimize survey strategies and understand biases.

Contribution

It develops a new simulation code (SNANA) that generates realistic supernova light curves and evaluates survey strategies using the Dark Energy Task Force figure of merit.

Findings

Simulations include atmospheric and intrinsic luminosity variations.

Analysis of selection biases for high-redshift supernovae.

Optimization of survey strategy to maximize scientific return.

Abstract

We present simulations for the Dark Energy Survey (DES) using a new code suite (SNANA) that generates realistic supernova light curves accounting for atmospheric seeing conditions and intrinsic supernova luminosity variations using MLCS2k2 or SALT2 models. Errors include stat-noise from photo-statistics and sky noise. We applied SNANA to simulate DES supernova observations and employed an MLCS-based fitter to obtain the distance modulus for each simulated light curve. We harnessed the light curves in order to study selection biases for high-redshift supernovae and to constrain the optimal DES observing strategy using the Dark Energy Task Force figure of merit.