Cosmological Evidence Modelling: a new simulationbased approach to constrain cosmology on nonlinear scales
Abstract
Extracting accurate cosmological information from galaxygalaxy and galaxymatter correlation functions on nonlinear scales ({≲ } 10 h^{1}{Mpc}) requires cosmological simulations. Additionally, one has to marginalize over several nuisance parameters of the galaxyhalo connection. However, the computational cost of such simulations prohibits naive implementations of stochastic posterior sampling methods like Markov chain Monte Carlo (MCMC) that would require of order O(10^6) samples in cosmological parameter space. Several groups have proposed surrogate models as a solution: a socalled emulator is trained to reproduce observables for a limited number of realizations in parameter space. Afterwards, this emulator is used as a surrogate model in an MCMC analysis. Here, we demonstrate a different method called Cosmological Evidence Modelling (CEM). First, for each simulation, we calculate the Bayesian evidence marginalized over the galaxyhalo connection by repeatedly populating the simulation with galaxies. We show that this Bayesian evidence is directly related to the posterior probability of cosmological parameters. Finally, we build a physically motivated model for how the evidence depends on cosmological parameters as sampled by the simulations. We demonstrate the feasibility of CEM by using simulations from the Aemulus simulation suite and forecasting cosmological constraints from BOSS CMASS measurements of redshiftspace distortions. Our analysis includes exploration of how galaxy assembly bias affects cosmological inference. Overall, CEM has several potential advantages over the more common approach of emulating summary statistics, including the ability to easily marginalize over highly complex models of the galaxyhalo connection and greater accuracy, thereby reducing the number of simulations required.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 December 2019
 DOI:
 10.1093/mnras/stz2664
 arXiv:
 arXiv:1909.03107
 Bibcode:
 2019MNRAS.490.1870L
 Keywords:

 methods: statistical;
 cosmological parameters;
 largescale structure of Universe;
 Astrophysics  Cosmology and Nongalactic Astrophysics;
 Astrophysics  Astrophysics of Galaxies
 EPrint:
 10 pages, 2 figures, submitted to MNRAS, comments welcome