Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
We use mock galaxy survey simulations designed to resemble the Dark Energy Survey Year 1 (DES Y1) data to validate and inform cosmological parameter estimation. When similar analysis tools are applied to both simulations and real survey data, they provide powerful validation tests of the DES Y1 cosmological analyses presented in companion papers. We use two suites of galaxy simulations produced using different methods, which therefore provide independent tests of our cosmological parameter inference. The cosmological analysis we aim to validate is presented in DES Collaboration et al. (2017) and uses angular two-point correlation functions of galaxy number counts and weak lensing shear, as well as their cross-correlation, in multiple redshift bins. While our constraints depend on the specific set of simulated realisations available, for both suites of simulations we find that the input cosmology is consistent with the combined constraints from multiple simulated DES Y1 realizations in the Omega(m) - sigma(8) plane. For one of the suites, we are able to show with high confidence that any biases in the inferred S-8 = sigma(8)(Omega(m)/0.3)(0.5) and Omega(m) are smaller than the DES Y1 1 - sigma uncertainties. For the other suite, for which we have fewer realizations, we are unable to be this conclusive; we infer a roughly 60 per cent (70 per cent) probability that systematic bias in the recovered Omega(m) (S-8) is sub-dominant to the DES Y1 uncertainty. As cosmological analyses of this kind become increasingly more precise, validation of parameter inference using survey simulations will be essential to demonstrate robustness.
Frédéric Courbin, Georges Meylan
Frédéric Courbin, Georges Meylan
Frédéric Courbin, Georges Meylan, Austin Chandler Peel