Generalized framework for likelihood-based field-level inference of growth rate from velocity and density fields

TL;DR

This paper introduces a unified, likelihood-based framework for nonlinear inference of the growth rate of large-scale structures using velocity and density fields, validated with simulations and capable of improved parameter estimation.

Contribution

It develops a generalized, efficient framework with a new covariance model including wide-angle corrections, enabling nonlinear field-level inference of $f\sigma_8$ and better Fisher forecasts.

Findings

Validated the framework against N-body simulations.

Achieved a 30% improvement in error estimation over standard Fisher forecasts.

Extended the wavenumber range for covariance calculations.

Abstract

Measuring the growth rate of large-scale structures (f) as a function of redshift has the potential to break degeneracies between modified gravity and dark energy models, when combined with expansion-rate probes. Direct estimates of peculiar velocities of galaxies have attracted interest as a means of estimating $f\sigma_8$. In particular, field-level methods can be used to fit the field nuisance parameter along with cosmological parameters simultaneously. This article aims to provide the community with a unified framework for the theoretical modeling of the likelihood-based field-level inference by performing fast field covariance calculations for velocity and density fields. Our purpose is to lay the foundations for a nonlinear extension of the likelihood-based method at the field level. We have developed a generalized framework, implemented in the dedicated software flip to perform a likelihood-based inference of $f\sigma_8$. We derived a new field covariance model, which includes wide-angle corrections. We also included the models previously described in the literature inside our framework. We compared their performance against ours, and we validated our model by comparing it with the two-point statistics of a recent N-body simulation. The tests we performed have allowed us to validate our software and determine the appropriate wavenumber range to integrate our covariance model and its validity in terms of separation. Our framework allows for a wider wavenumber coverage to be used in our calculations than in previous works. Finally, our generalized framework allows us to efficiently perform a survey geometry-dependent Fisher forecast of the $f\sigma_8$ parameter. We show that the Fisher forecast method we developed gives an error bar that is 30 % closer to a full likelihood-based estimation than a standard volume Fisher forecast.