dc.contributor.author | Hahn, ChangHoon | |
dc.contributor.author | Beutler, Florian | |
dc.contributor.author | Sinha, Manodeep | |
dc.contributor.author | Berlind, Andreas | |
dc.contributor.author | Ho, Shirley | |
dc.contributor.author | Hogg, David W. | |
dc.date.accessioned | 2019-09-24T17:22:11Z | |
dc.date.available | 2019-09-24T17:22:11Z | |
dc.date.issued | 2019-02-26 | |
dc.identifier.citation | ChangHoon Hahn, Florian Beutler, Manodeep Sinha, Andreas Berlind, Shirley Ho, David W Hogg, Likelihood non-Gaussianity in large-scale structure analyses, Monthly Notices of the Royal Astronomical Society, Volume 485, Issue 2, May 2019, Pages 2956–2969 | en_US |
dc.identifier.uri | http://hdl.handle.net/1803/9530 | |
dc.description.abstract | Standard present-day large-scale structure (LSS) analyses make a major assumption in their Bayesian parameter inference – that the likelihood has a Gaussian form. For summary statistics currently used in LSS, this assumption, even if the underlying density field is Gaussian, cannot be correct in detail. We investigate the impact of this assumption on two recent LSS analyses: the Beutler et al. power spectrum multipole (Pℓ) analysis and the Sinha et al. group multiplicity function (ζ) analysis. Using non-parametric divergence estimators on mock catalogues originally constructed for covariance matrix estimation, we identify significant non-Gaussianity in both the Pℓ and ζ likelihoods. We then use Gaussian mixture density estimation and independent component analysis on the same mocks to construct likelihood estimates that approximate the true likelihood better than the Gaussian pseudo-likelihood. Using these likelihood estimates, we accurately estimate the true posterior probability distribution of the Beutler et al. and Sinha et al. parameters. Likelihood non-Gaussianity shifts the fσ8 constraint by −0.44σ, but otherwise does not significantly impact the overall parameter constraints of Beutler et al. For the ζ analysis, using the pseudo-likelihood significantly underestimates the uncertainties and biases the constraints of the Sinha et al. halo occupation parameters. For logM1 and α, the posteriors are shifted by +0.43σ and −0.51σ and broadened by 42 per cent and 66 per cent, respectively. The divergence and likelihood estimation methods we present provide a straightforward framework for quantifying the impact of likelihood non-Gaussianity and deriving more accurate parameter constraints. | en_US |
dc.description.sponsorship | This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics, under contract No. DE-AC02-05CH11231. This project used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Parts of this research were conducted by the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE170100013. This project also made use of the NASA Astrophysics Data System and open-source software python, numpy, SciPy, matplotlib, and scikit-learn. | en_US |
dc.publisher | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | en_US |
dc.subject | methods: data analysis | en_US |
dc.subject | methods: statistical | en_US |
dc.subject | galaxies: statistics | en_US |
dc.subject | cosmology: observations | en_US |
dc.subject | cosmological parameters | en_US |
dc.subject | large-scale structure of the universe | en_US |
dc.subject.lcsh | Cosmology--Methodology. | en_US |
dc.title | Likelihood non-Gaussianity in Large-scale Structure Analyses | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1093/mnras/stz558 | |
dcterms.rights | Published by Oxford University Press on behalf of the Royal Astronomical Society 2019. This work is written by (a) US Government employee(s) and is in
the public domain in the US. | |