Sensitivity of Cosmological Parameter Estimation to Nonlinear Prescription from Galaxy Clustering
Sarah Safi, Marzieh Farhang

TL;DR
This paper assesses how different nonlinear modeling schemes impact the accuracy of cosmological parameter estimation from galaxy clustering data, highlighting biases and the importance of nuisance parameter marginalization.
Contribution
It evaluates the sensitivity of parameter inference to nonlinear prescriptions, especially Halofit and EFTofLSS, and emphasizes the need for better nuisance parameter modeling.
Findings
Significant biases (~5σ) arise from nonlinear prescription choice.
Marginalizing over nuisance parameters increases errors, reducing differences between models.
More accurate nuisance parameter modeling enhances cosmological constraints.
Abstract
Next generation large scale surveys probe the nonlinear regime with high resolution. Making viable cosmological inferences based on these observations requires accurate theoretical modeling of the mildly nonlinear regime. In this work we investigate the sensitivity of cosmological parameter measurements from future probes of galaxy clustering to the choice of nonlinear prescription up to . In particular, we calculate the induced parameter bias when the mildly nonlinear regime is modeled by the Halofit fitting scheme. We find significant () bias for some parameters with a future Euclid-like survey. We also explore the contribution of different scales to the parameter estimation for different observational setups and cosmological scenarios, compared for the two nonlinear prescriptions of Halofit and EFTofLSS. We include in the analysis the…
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