Synthetic Cosmological Data for Multi-Survey Analysis in the 2020s
Abstract:
The next generation of large imaging and spectroscopic surveys such as Rubin, Roman, Euclid, and DESI will provide a multi-wavelength view of the cosmos with unprecedented precision. The advent of these large datasets heralds a new era in cosmology that is characterized by small statistical uncertainties, improved control of systematic errors, and unique opportunities to combine multiple probes of fundamental physics. Mock galaxy catalogs play a number of essential roles that provide mission-critical support for these surveys: these mocks serve as testbeds to study questions of survey design, they enable studies of possible systematics, and they facilitate tests of data reduction pipelines. Historically, cosmological surveys have typically developed their own mock data from scratch, requiring a considerable effort of both human and computational resources, even though much of this labor and associated infrastructure ends up being repeated from survey to survey. In this talk, I will discuss a new effort supported by NASA to develop common infrastructure to support the next generation of cosmology surveys. I will present recent progress in developing flexible techniques that can be applied to a wide variety of datasets, with special focus on methodologies that effectively leverage high-performance computational resources.
