The Clustering of Little Red Dots from Ultra-Strongly Self-Interacting Dark Matter

TL;DR

This paper predicts the clustering bias of Little Red Dots (LRDs) at high redshift based on a model of ultra-strongly self-interacting dark matter, providing a novel theoretical framework for understanding their distribution.

Contribution

It offers the first formation-based theoretical prediction of LRD clustering consistent with their mass function, independent of uSIDM microphysics parameters.

Findings

Bias parameter $b_{eff} \\sim 4.5$ at $z\sim5$

LRDs occupy halos of ~$8\times10^{10} M_{\\odot}$

Clustering predictions are robust across uSIDM parameters

Abstract

We predict the effective clustering bias parameter, $b_{\rm{eff}}$, at $z\sim5$ for Little Red Dots (LRDs) seeded by Ultra-Strongly Self-Interacting Dark Matter (uSIDM). From our model, we find that $b_{\rm{eff}}\sim4.5$, thus we infer that LRDs seeded by uSIDM would populate halos of typical masses $\sim 8\times10^{10}~M_{\odot}$; this bias factor is consistent with LRDs being a distinct population from high redshift quasars. To the extent that we are aware, this is the first formation-based theoretical prediction of LRD clustering from a model consistent with the LRD mass function. We find that this bias and clustering is insensitive to a wide range of the underlying uSIDM microphysics parameters, including the uSIDM cross-section $\sigma/m$ and uSIDM fraction $f$. This is therefore a robust prediction from the uSIDM model, and will allow for direct probes of the uSIDM paradigm as the origin of LRDs in the next few years. Upcoming \texttt{JWST} observations will constrain the population of LRDs, including directly measuring their clustering.