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

Journal Article · Tue Sep 24 00:00:00 EDT 2019 · Monthly Notices of the Royal Astronomical Society

DOI:https://doi.org/10.1093/mnras/stz2664· OSTI ID:1576764

Lange, Johannes U.  ^[1]; van den Bosch, Frank C.  ^[1]; Zentner, Andrew R.  ^[2]; Wang, Kuan  ^[2]; Hearin, Andrew P. ^[3]; Guo, Hong ^[4]

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1. Yale Univ., New Haven, CT (United States)
2. Univ. of Pittsburgh, PA (United States)
3. Argonne National Lab. (ANL), Argonne, IL (United States)
4. Shanghai Astronomical Observatory, Shanghai (China)

Extracting accurate cosmological information from galaxy–galaxy and galaxy–matter correlation functions on non-linear scales ($${\lesssim } 10 \, h^{-1}{\rm {Mpc}}$$) requires cosmological simulations. Additionally, one has to marginalize over several nuisance parameters of the galaxy–halo 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 $$\mathcal {O}(10^6)$$ samples in cosmological parameter space. Several groups have proposed surrogate models as a solution: a so-called 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. In this work, we demonstrate a different method called Cosmological Evidence Modelling (CEM). First, for each simulation, we calculate the Bayesian evidence marginalized over the galaxy–halo 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 redshift-space 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 galaxy–halo connection and greater accuracy, thereby reducing the number of simulations required.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: National Science Foundation (NSF); National Aeronautics and Space Administration (NASA); National Key Basic Research Program of China; National Natural Science Foundation of China (NSFC); USDOE Office of Science (SC), High Energy Physics (HEP); Klaus Tschira Foundation
• Grant/Contract Number: AC02-06CH11357; AC02-05CH11231; AST 1516962; 17- ATP17-0028; AST 1517563
• OSTI ID: 1576764
• Journal Information: Monthly Notices of the Royal Astronomical Society, Vol. 490, Issue 2; ISSN 0035-8711
• Publisher: Royal Astronomical Society
• Country of Publication: United States
• Language: English

Related Subjects

79 ASTRONOMY AND ASTROPHYSICS
cosmological parameters
large-scale structure of Universe
methods: statistical