Lagrangian Bias as a Gaussian Random Field

TL;DR

This paper presents a novel framework where halo bias is modeled as a Gaussian random field in Lagrangian space, offering insights into halo clustering and bias distribution.

Contribution

It introduces a scale-independent Lagrangian bias field concept, extending to secondary biases, and predicts halo bias relations from geometric selection.

Findings

Predicts the $b(M)$ relation for halos.

Explains the Gaussian distribution of halo bias at fixed mass.

Provides a foundation for an ab initio halo clustering model.

Abstract

Halo bias is typically treated as a set of coefficients in a perturbative expansion. We show instead that every point in a Gaussian density field has a well-defined scale-independent Lagrangian bias, thereby defining a bias field. This property can be extended to any linear operator acting on the Lagrangian density field, generating secondary bias fields. Halo bias then arises from geometric selection of Lagrangian patches within this pre-existing field, rather than being generated by collapse. We demonstrate that this framework predicts the measured $b(M)$ relation for halos. The multivariate Gaussian structure of the fields naturally explains the Gaussian distribution of halo bias at fixed mass and assembly bias. The results presented here motivate combining this framework with a forward model of halo collapse, yielding an ab initio model for halo clustering.