Bounding the Effect of HOD Assumptions on Small-Scale Clustering Constraints

Abstract

Small-scale galaxy clustering is expected to contain substantial cosmological information, but the extent to which this information constrains halo-based cosmologies independent of an assumed galaxy--halo connection remains unclear. We quantify this dependence using LRG-like mock galaxy catalogs built from 81 cosmologies in the {\tt \textsc{AbacusSummit}} suite. We analyze two-point correlation function multipoles on scales ranging from $5$--$80$ Mpc/$h$ and compare two limiting treatments, the \enquote{floor} and \enquote{ceiling}, of the standard five-parameter HOD model. In the conservative floor case, we impose only broad initial HOD bounds and profile over HOD parameters to determine the minimum constraining power available; we accomplish this with {\tt HODmin}, a two-stage global optimization algorithm written for minimizing $\chi^2$ in HOD space. In the optimistic ceiling case, we assume the HOD parameters are known exactly. We find a significant difference between the floor and ceiling when comparing against the same Planck $\Lambda$CDM mock data vector with identical modeling assumptions: for the floor, $25\%$ of the discrete {\tt \textsc{AbacusSummit}} cosmologies tested are excluded at $3\sigma$, whereas for the ceiling, $\sim81\%$ are excluded. Many cosmologies agree well with data in the floor, and yet in the ceiling are excluded by multiple orders of magnitude in $\chi^2$. We therefore observe the strength of small-scale clustering constraints depends heavily on the amount of prior HOD information assumed. We compare the sensitivity of this effect to various choices like scale cut, angle cut, multipole inclusion, mock phase, and mock HOD model. Our wide floor--ceiling bracket indicates that informative galaxy--halo priors are necessary for extracting strong small-scale clustering constraints.