Galaxy-Multiplet Clustering from DESI DR2

TL;DR

This paper introduces an efficient method for measuring higher-order galaxy clustering using multiplets from DESI DR2, revealing their stronger bias and limitations of current models, and demonstrating the benefits of including secondary biases.

Contribution

The study presents a novel estimator for galaxy multiplet clustering, shows their stronger bias compared to individual galaxies, and demonstrates how incorporating secondary biases improves model agreement.

Findings

Multiplets exhibit stronger clustering bias than individual galaxies.

Mock catalogs underpredict multiplet clustering, indicating model limitations.

Including secondary biases in HOD models improves fit to observed data.

Abstract

We present an efficient estimator for higher-order galaxy clustering using small groups of nearby galaxies, or multiplets. Using the Luminous Red Galaxy (LRG) sample from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2, we identify galaxy multiplets as discrete objects and measure their cross-correlations with the general galaxy field. Our results show that the multiplets exhibit stronger clustering bias as they trace more massive dark matter halos than individual galaxies. When comparing the observed clustering statistics with the mock catalogs generated from the N-body simulation AbacusSummit, we find that the mocks underpredict multiplet clustering despite reproducing the galaxy two-point auto-correlation reasonably well. This discrepancy indicates that the standard Halo Occupation Distribution (HOD) model is insufficient to describe the properties of galaxy multiplets, revealing the greater constraining power of this higher-order statistic on galaxy-halo connection and the possibility that multiplets are specific to additional assembly bias. We demonstrate that incorporating secondary biases into the HOD model improves agreement with the observed multiplet statistics, specifically by allowing galaxies to preferentially occupy halos in denser environments. Our results highlight the potential of utilizing multiplet clustering, beyond traditional two-point correlation measurements, to break degeneracies in models describing the galaxy-dark matter connection.