On priors and scale cuts in EFT-based full-shape analyses

Abstract

Parameter estimation from galaxy survey data from the full-shape method depends on scale cuts and priors on EFT parameters. The effects of priors, including the so-called ''prior volume'' phenomenon have been originally studied in Ivanov et al. (2019) and subsequent works. In this note, we repeat and extend these tests and also apply them to other priors used in the literature. We point out that in addition to the ''prior volume'' effect there is a more dangerous effect that is largely overlooked: a systematic bias on cosmological parameters due to overoptimistic scale cuts. Unlike the ''prior volume'' effect, this is a genuine systematic bias due to two-loop corrections that does not vanish with better priors or with larger data volumes. Our study is based on the high fidelity BOSS-like PT Challenge simulation data which offer many advantages over analyses based on synthetic data generated with fitting pipelines. We show that some analysis choices associated with the PyBird code, especially the scale cuts, significantly bias parameter recovery, overestimating $\sigma_8$ by over $5\%$ (equivalent to $1\sigma$). The bias on measured EFT parameters is even more significant. In contrast, the analysis choices associated with the CLASS-PT code lead to much smaller ($\lesssim 1\%$) shifts in cosmological parameters based on their best-fit values.