Nonlinear reconstruction of features in the primordial power spectrum from large-scale structure

TL;DR

This paper demonstrates that nonlinear reconstruction techniques can effectively recover damped primordial features in the large-scale structure, enhancing the ability of galaxy surveys to constrain early Universe models.

Contribution

It introduces a nonlinear reconstruction method that restores primordial features in galaxy data, improving parameter constraints at lower cost than expanding survey scope.

Findings

Nonlinear reconstruction recovers damped primordial oscillations.

Improves feature parameter measurement accuracy.

Enables stronger constraints with less survey volume.

Abstract

Potential features in the primordial power spectrum have been searched for in galaxy surveys in recent years since these features can assist in understanding the nature of inflation. The null detection to date suggests that any such features should be fairly weak, and next-generation galaxy surveys, with their unprecedented sizes and precisions, are in a position to place stronger constraints than before. However, even if such primordial features once existed in the early Universe, they would have been significantly damped in the nonlinear regime at low redshift due to structure formation, which makes them difficult to be directly detected in real observations. A potential way to tackle this challenge for probing the features is to undo the cosmological evolution, i.e., using reconstruction to obtain an approximate linear density field. By employing a set of N-body simulations, here we show that a recently-proposed nonlinear reconstruction algorithm can effectively retrieve damped oscillatory features from halo catalogues and improve the accuracy of the measurement of feature parameters (assuming that such primordial features do exist). We do a Fisher analysis to forecast how nonlinear reconstruction affects the constraining power, and find that it can lead to significantly more robust constraints on the feature amplitude for a DESI-like survey. Comparing nonlinear reconstruction with other ways of improving constraints, such as increasing the survey volume and range of scales, this shows that it is possible to achieve what the latter do, but at a lower cost.