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arXiv 2309.04468astro-ph.COhep-ph
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基于贝叶斯和频率派方法对全形状BOSS和eBOSS数据的EFTofLSS分析中先验效应的研究

Bayesian and frequentist investigation of prior effects in EFTofLSS analyses of full-shape BOSS and eBOSS data

Emil Brinch Holm, Laura Herold, Théo Simon, Elisa G. M. Ferreira, Steen Hannestad, Vivian Poulin, Thomas Tram

AI总结:

本研究采用基于轮廓似然的频率派方法分析BOSS、eBOSS和Planck数据,发现EFTofLSS分析中贝叶斯先验具有信息量且先验体积效应显著,导致参数偏移和区间变宽,强调需结合频率派与贝叶斯方法进行宇宙学推断。

摘要:

先前基于贝叶斯方法的研究表明,使用大尺度结构有效场理论(EFTofLSS)从重子振荡光谱巡天(BOSS)全形状数据中得到的宇宙学参数约束,依赖于对EFT nuisance参数先验的选择。在这项工作中,我们通过采用基于轮廓似然方法的频率派方法来探索这种先验依赖性,该方法本质上独立于先验,并考虑了来自BOSS、eBOSS和Planck的数据。我们发现,贝叶斯推断中对EFT参数的先验是提供信息的,并且先验体积效应很重要。这反映在基线配置下,对于BOSS(eBOSS),后验均值与最大似然估计相比偏移高达1.0σ(1.6σ),并且与贝叶斯区间相比,由频率派得出的区间变宽了高达1.9(1.6)倍,而来自Planck的约束保持不变。我们的频率派置信区间没有表明BOSS/eBOSS与Planck之间存在张力。然而,我们发现轮廓似然倾向于EFT参数的极端值,这突出了将贝叶斯和频率派方法结合起来以进行细致入微的宇宙学推断的重要性。我们展示了未来数据统计能力的提升将调和使用EFTofLSS得出的频率派和贝叶斯推断的约束。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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