Bayesian and frequentist investigation of prior effects in EFTofLSS analyses of
Words of the editor about this manuscript.
The product being described is an article published in the SCI-indexed monthly journal, Physics Tomorrow Theoretical Physics Letters (TPL). The article investigates the prior effects in EFTofLSS analyses of full-shape BOSS and eBOSS data using both Bayesian and frequentist approaches. The article is based on a thorough peer-review process, ensuring its academic rigor and reliability. As a theoretical physics journal, TPL is dedicated to publishing high-quality research in the field of physics, making this article a valuable contribution to the scientific community. The article is written in an academic style, using technical language and precise terminology to convey its findings.
Previous studies based on Bayesian methods have shown that the constraints on cosmological parameters from the Baryonic Oscillation Spectroscopic Survey (BOSS) full-shape data using the Effective Field Theory of Large Scale Structure (EFTofLSS) depend on the choice of prior on the EFT nuisance parameters. In this work, we explore this prior dependence by adopting a frequentist approach based on the profile likelihood method, which is inherently independent of priors, considering data from BOSS, eBOSS and Planck. We find that the priors on the EFT parameters in the Bayesian inference are informative and that prior volume effects are important. This is reflected in shifts of the posterior mean compared to the maximum likelihood estimate by up to 1.0 σ (1.6 σ) and in a widening of intervals informed from frequentist compared to Bayesian intervals by factors of up to 1.9 (1.6) for BOSS (eBOSS) in the baseline configuration, while the constraints from Planck are unchanged. Our frequentist confidence intervals give no indication of a tension between BOSS/eBOSS and Planck. However, we find that the profile likelihood prefers extreme values of the EFT parameters, highlighting the importance of combining Bayesian and frequentist approaches for a fully nuanced cosmological inference. We show that the improved statistical power of future data will reconcile the constraints from frequentist and Bayesian inference using the EFTofLSS.