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          Institute: MPI für Astronomie     Collection: Publikationen_mpia     Display Documents



ID: 742690.0, MPI für Astronomie / Publikationen_mpia
Approximate Bayesian computation in large-scale structure: constraining the galaxy-halo connection
Authors:Hahn, C.; Vakili, M.; Walsh, K.; Hearin, A. P.; Hogg, D. W.; Campbell, D.
Date of Publication (YYYY-MM-DD):2017
Title of Journal:Monthly Notices of the Royal Astronomical Society
Volume:469
Issue / Number:3
Start Page:2791
End Page:2805
Audience:Not Specified
Abstract / Description:Standard approaches to Bayesian parameter inference in large-scale structure assume a Gaussian functional form (chi-squared form) for the likelihood. This assumption, in detail, cannot be correct. Likelihood free inferences such as approximate Bayesian computation (ABC) relax these restrictions and make inference possible without making any assumptions on the likelihood. Instead ABC relies on a forward generative model of the data and a metric for measuring the distance between the model and data. In this work, we demonstrate that ABC is feasible for LSS parameter inference by using it to constrain parameters of the halo occupation distribution (HOD) model for populating dark matter haloes with galaxies. Using specific implementation of ABC supplemented with population Monte Carlo importance sampling, a generative forward model using HOD and a distance metric based on galaxy number density, two-point correlation function and galaxy group multiplicity function, we constrain the HOD parameters of mock observation generated from selected `true' HOD parameters. The parameter constraints we obtain from ABC are consistent with the `true' HOD parameters, demonstrating that ABC can be reliably used for parameter inference in LSS. Furthermore, we compare our ABC constraints to constraints we obtain using a pseudo-likelihood function of Gaussian form with MCMC and find consistent HOD parameter constraints. Ultimately, our results suggest that ABC can and should be applied in parameter inference for LSS analyses.
Free Keywords:methods: data analysis; methods: statistical; galaxies: haloes; dark matter; large-scale structure of Universe
External Publication Status:published
Document Type:Article
Communicated by:N. N.
Affiliations:MPI für Astronomie
Identifiers:ISSN:0035-8711
URL:http://adsabs.harvard.edu/abs/2017MNRAS.469.2791H
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