Probing features in the primordial perturbation spectrum with large-scale structure data

TL;DR

This paper investigates how large-scale structure data, specifically three-dimensional density fields, can better differentiate between primordial power spectrum models with features that are indistinguishable by CMB data alone, using simulations.

Contribution

It demonstrates that traditional statistics like halo mass function and correlation functions are insufficient, proposing count-in-cell density fields as a more effective method for model discrimination.

Findings

Halo mass function and correlation functions cannot distinguish models.

Count-in-cell density fields outperform traditional statistics in model differentiation.

Large-scale structure data can help identify features in the primordial power spectrum.

Abstract

The form of the primordial power spectrum (PPS) of cosmological scalar (matter density) perturbations is not yet constrained satisfactorily in spite of the tremendous amount of information from the Cosmic Microwave Background (CMB) data. While a smooth power-law-like form of the PPS is consistent with the CMB data, some PPS with small non-smooth features at large scales can also fit the CMB temperature and polarization data with similar statistical evidence. Future CMB surveys cannot help distinguish all such models due to the cosmic variance at large angular scales. In this paper, we study how well we can differentiate be- tween such featured forms of the PPS not otherwise distinguishable using CMB data. We ran 15 N-body DESI-like simulations of these models to explore this approach. Showing that statistics such as the halo mass function and the two-point correlation function are not able to distinguish these models in a DESI-like survey, we advocate to avoid reducing the dimensionality of the problem by demonstrating that the use of a simple three-dimensional count-in-cell density field can be much more effective for the purpose of model distinction.