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

Abstract

Extracting accurate cosmological information from galaxy-galaxy and galaxy-matter correlation functions on non-linear scales (less than or similar to 10 h(-1) 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 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. Here, 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-spacemore » 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.« less

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science - Office of High Energy Physics; National Science Foundation (NSF); National Aeronautic and Space Administration (NASA); National Key Basic Research Program of China; National Natural Science Foundation of China (NNSFC)
OSTI Identifier:
1576764
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
Monthly Notices of the Royal Astronomical Society
Additional Journal Information:
Journal Volume: 490; Journal Issue: 2
Country of Publication:
United States
Language:
English
Subject:
cosmological parameters; large-scale structure of Universe; methods: statistical

Citation Formats

Lange, Johannes U., van den Bosch, Frank C., Zentner, Andrew R., Wang, Kuan, Hearin, Andrew P, and Guo, Hong. Cosmological Evidence Modelling: a new simulation-based approach to constrain cosmology on non-linear scales. United States: N. p., 2019. Web. doi:10.1093/mnras/stz2664.
Lange, Johannes U., van den Bosch, Frank C., Zentner, Andrew R., Wang, Kuan, Hearin, Andrew P, & Guo, Hong. Cosmological Evidence Modelling: a new simulation-based approach to constrain cosmology on non-linear scales. United States. doi:10.1093/mnras/stz2664.
Lange, Johannes U., van den Bosch, Frank C., Zentner, Andrew R., Wang, Kuan, Hearin, Andrew P, and Guo, Hong. Sun . "Cosmological Evidence Modelling: a new simulation-based approach to constrain cosmology on non-linear scales". United States. doi:10.1093/mnras/stz2664.
@article{osti_1576764,
title = {Cosmological Evidence Modelling: a new simulation-based approach to constrain cosmology on non-linear scales},
author = {Lange, Johannes U. and van den Bosch, Frank C. and Zentner, Andrew R. and Wang, Kuan and Hearin, Andrew P and Guo, Hong},
abstractNote = {Extracting accurate cosmological information from galaxy-galaxy and galaxy-matter correlation functions on non-linear scales (less than or similar to 10 h(-1) 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 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. Here, 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.},
doi = {10.1093/mnras/stz2664},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 2,
volume = 490,
place = {United States},
year = {2019},
month = {12}
}

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