Galaxy-Multiplet Clustering from DESI DR2

TL;DR

This work tackles how higher-order galaxy clustering via small galaxy multiplets can tighten constraints on the galaxy-dark matter connection beyond traditional two-point statistics. By identifying multiplets in DESI DR2 LRGs and cross-correlating them with the general galaxy field, the study compares observations to AbacusSummit mocks with vanilla and extended HODs, after calibrating fiber assignment effects. It finds that vanilla HODs underpredict multiplet clustering, while environment-based secondary biases significantly improve agreement, highlighting assembly bias as a key ingredient and demonstrating the utility of multiplet statistics to break degeneracies in galaxy-halo models. The results imply multiplet clustering is a powerful, computationally efficient probe for galaxy assembly history and cosmology, motivating future full-likelihood analyses and integration with lensing and multi-tracer approaches as DESI data grow.

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.

Figures (6)

• Figure 4: Comparison between the auto-correlations and the multiplet cross-correlations calculated from mocks without and with fiber assignment effects (the complete and altmtl mocks). The color differences follow the convention of this paper, while dark colors mark results from the complete mocks and light colors represent results from the altmtl mocks.
• Figure 5: Comparison between the galaxy-multiplet cross-correlations measured from the Y3 observations and the altmtl mocks. Following Fig. \ref{['fig:cross-corr']}, each color represents clustering measured with galaxy multiplets of different sizes, with lighter colors indicating the mock results and darker colors depicting the observations.
• Figure 6: Comparison between the general clustering amplitudes for galaxies and multiplets, represented by the projected correlation functions averaged over a specific $r_{p}$ range. Triangular points in lighter colors show results from the mocks, while darker circular points depict the observations. Two versions of error bars are included: jackknife errors estimated from the observations are shown on the left, and error bars capturing the scatter between mocks are on the right.
• Figure 7: The average clustering amplitudes measured from observations and predicted by HOD models with different concentration-based secondary biases added to the same set of baseline parameters. Observational data points with jackknife error bars are fixed at zero as references. Models with different secondary biases, as indicated in the labels, are plotted from left to right on either side of the baseline model results.
• Figure 8: Example of average clustering amplitudes measured from observations and predicted by HOD models incorporating different concentration-based secondary biases of enhanced magnitudes. Observational data points with jackknife error bars are fixed at zero as references. Predictions from the baseline HOD model and from models incorporating positive and negative $A_{c}$ are represented by circles, triangles, and squares, respectively.