Supernova Cosmology in the Big Data Era

Abstract

Here we describe large “Big Data” Supernova (SN) Ia surveys, past and present, used to make precision measurements of cosmological parameters that describe the expansion history of the universe. In particular, we focus on surveys designed to measure the dark energy equation of state parameter w and its dependence on cosmic time. These large surveys have at least four photometric bands, and they use a rolling search strategy in which the same instrument is used for both discovery and photometric follow-up observations. These surveys include the Supernova Legacy Survey (SNLS), Sloan Digital Sky Survey II (SDSS- II), Pan-STARRS 1 (PS1), Dark Energy Survey (DES), and Large Synoptic Survey Telescope (LSST). We discuss the development of how systematic uncertainties are evaluated, and how methods to reduce them play a major role is designing new surveys. The key systematic effects that we discuss are (1) calibration, measuring the telescope efficiency in each filter band, (2) biases from a magnitude-limited survey and from the analysis, and (3) photometric SN classification for current surveys that don’t have enough resources to spectroscopically confirm each SN candidate.