Cosmology on point: modelling spectroscopic tracer one-point statistics

TL;DR

This paper extends matter density PDF predictions to biased tracers like halos and galaxies, demonstrating their potential to improve cosmological parameter constraints and complement power spectrum analyses.

Contribution

It introduces a model for tracer count PDFs incorporating bias and stochasticity, validated against simulations, and explores their cosmological constraining power.

Findings

Accurate bias parametrization with Gaussian Lagrangian bias model.

Excellent agreement between model predictions and N-body simulations.

Tracer PDFs help disentangle bias from cosmological parameters.

Abstract

The 1-point matter density probability distribution function (PDF) captures some of the non-Gaussian information lost in standard 2-point statistics. The matter PDF can be well predicted at mildly non-linear scales using large deviations theory. This work extends those predictions to biased tracers like dark matter halos and the galaxies they host. We model the conditional PDF of tracer counts given matter density using a tracer bias and stochasticity model previously used for photometric data. We find accurate parametrisations for tracer bias with a smoothing scale-independent 2-parameter Gaussian Lagrangian bias model and a quadratic shot noise. We relate those bias and stochasticity parameters to the one for the power spectrum and tracer-matter covariances. We validate the model against the Quijote suite of N-body simulations and find excellent agreement for both halo and galaxy density PDFs and their cosmology dependence. We demonstrate the constraining power of the tracer PDFs and their complementarity to power spectra through a Fisher forecast. We focus on the cosmological parameters $\sigma_8$ and $\Omega_m$ as well as linear bias parameters, finding that the strength of the tracer PDF lies in disentangling tracer bias from cosmology. Our results show promise for applications to spectroscopic clustering data when augmented with a redshift space distortion model