Analysis of unified galaxy power spectrum multipole measurements

Jamie Donald-McCann*, Rafaela Gsponer, Ruiyang Zhao, Kazuya Koyama, Florian Beutler

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We present a series of full-shape analyses of galaxy power spectrum multipole measurements from the 6dFGS, BOSS, and eBOSS galaxy surveys. We use an emulated effective field theory of large-scale structure (EFTofLSS) model to conduct these analyses. We exploit the accelerated prediction speed of the neural-network-based emulator to explore various analysis setups for our cosmological inference pipeline. Via a set of mock full-shape analyses of synthetic power spectrum multipoles, designed to approximate measurements from the surveys above, we demonstrate that the use of alternative priors on nuisance parameters and restricted model complexity reduces many of the biases previously observed in marginalized cosmological constraints coming from EFTofLSS analyses. The alternative priors take the form of a Jeffreys prior; a non-informative prior that can mitigate against biases induced by marginalizing over poorly constrained nuisance parameters. When performing a joint analysis of all synthetic multipoles, we see an improvement in the level of agreement between the marginalized ln (1010As) constraints and the truth; from ∼2.0σ to ∼0.42σ. Using our pipeline to analyse the measured multipoles, we find an improvement in the level of agreement with cosmic microwave background (CMB) results; from ∼2.4σ to ∼0.5σ. Therefore, we conclude that the spectroscopic galaxy survey data sets listed above are consistent with constraints obtained from the CMB.

Original languageEnglish
Pages (from-to)3461-3481
Number of pages21
JournalMonthly Notices of the Royal Astronomical Society
Volume526
Issue number3
Early online date28 Sept 2023
DOIs
Publication statusPublished - 1 Dec 2023

Keywords / Materials (for Non-textual outputs)

  • cosmology: cosmological parameters
  • large-scale structure of Universe
  • methods: data analysis

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