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Extracting accurate cosmological information from galaxy-galaxy andgalaxy-matter correlation functions on non-linear scales ($\lesssim 10 h^{-1}\mathrm{Mpc}$) requires cosmological simulations. Additionally, one has tomarginalise over several nuisance parameters of the galaxy-halo connection.However, the computational cost of such simulations prohibits naiveimplementations of stochastic posterior sampling methods like Markov chainMonte Carlo (MCMC) that would require of order $\mathcal{O}(10^6)$ samples incosmological parameter space. Several groups have proposed surrogate models asa solution: a so-called emulator is trained to reproduce observables for alimited number of realisations in parameter space. Afterwards, this emulator isused as a surrogate model in an MCMC analysis. Here, we demonstrate a differentmethod called Cosmological Evidence Modelling (CEM). First, for eachsimulation, we calculate the Bayesian evidence marginalised over thegalaxy-halo connection by repeatedly populating the simulation with galaxies.We show that this Bayesian evidence is directly related to the posteriorprobability of cosmological parameters. Finally, we build a physicallymotivated model for how the evidence depends on cosmological parameters assampled by the simulations. We demonstrate the feasibility of CEM by usingsimulations from the Aemulus simulation suite and forecasting cosmologicalconstraints from BOSS CMASS measurements of redshift-space distortions. Ouranalysis includes an exploration of how galaxy assembly bias affectscosmological inference. Overall, CEM has several potential advantages over themore common approach of emulating summary statistics, including the ability toeasily marginalise over highly complex models of the galaxy-halo connection andgreater accuracy, thereby reducing the number of simulations required.

Cosmological Evidence Modelling: a new simulation-based approach to constrain cosmology on non-linear scales