Full forward model of galaxy clustering statistics with AbacusSummit lightcones

TL;DR

This paper develops a comprehensive forward modeling pipeline for galaxy clustering statistics using AbacusSummit lightcones, enabling unbiased analysis of small-scale clustering and beyond-2PCF statistics like kNN, with applications to upcoming surveys.

Contribution

The paper introduces a flexible, efficient forward modeling approach that accounts for systematic effects and allows for unbiased inference of galaxy-halo connections using multiple summary statistics.

Findings

Successfully recovers unbiased galaxy-halo connection constraints.

Demonstrates the effectiveness of kNN statistics in capturing higher-order correlations.

Provides a foundation for future cosmology emulators with multiple summary statistics.

Abstract

Novel summary statistics beyond the standard 2-point correlation function (2PCF) are necessary to capture the full astrophysical and cosmological information from the small-scale (r < 30Mpc/h) galaxy clustering. However, the analysis of beyond-2PCF statistics on small scales is challenging because we lack the appropriate treatment of observational systematics for arbitrary summary statistics of the galaxy field. In this paper, we develop a full forward modeling pipeline for a wide range of summary statistics using the large high-fidelity AbacusSummit lightcones that accounts for many systematic effects but also remains flexible and computationally efficient to enable posterior sampling. We apply our forward model approach to a fully realistic mock galaxy catalog and demonstrate that we can recover unbiased constraints on the underlying galaxy-halo connection model using two separate summary statistics: the standard 2PCF and the novel k-th nearest neighbor (kNN) statistics, which are sensitive to correlation functions of all orders. We will extend this method to a full cosmology emulator in a follow up paper. We expect this to become a powerful approach when applying to upcoming surveys such as DESI where we can leverage a multitude of summary statistics across a wide redshift range to maximally extract information from the non-linear scales.