Resummed Perturbation Theory of Galaxy Clustering

TL;DR

This paper develops a resummed perturbation theory model for galaxy clustering that combines Eulerian and Lagrangian approaches, improving accuracy at quasi-linear scales and incorporating galaxy formation history.

Contribution

It introduces a novel combined Eulerian-Lagrangian resummed perturbation theory model for galaxy clustering, enhancing convergence and accounting for galaxy formation history.

Findings

Exponential damping of multipoint propagators improves statistical convergence.

The model accurately describes galaxy clustering at quasi-linear scales.

Incorporates non-local gravitational effects and galaxy formation history.

Abstract

The relationship between observed tracers such as galaxies and the underlying dark matter distribution is crucial in extracting cosmological information. As the linear bias model breaks down at quasi-linear scales, the standard perturbative approach of the nonlinear Eulerian bias model (EBM) is not accurate enough in describing galaxy clustering. In this paper, we discuss such a model in the context of resummed perturbation theory, and further generalize it to incorporate the subsequent gravitational evolution by combining with a Lagrangian description of galaxies' motion. The multipoint propagators we constructed for such model also exhibit exponential damping similar to their dark matter counterparts, therefore the convergence property of statistics built upon these quantities is improved. This is achieved by applying both Eulerian and Lagrangian resummation techniques of dark matter field developed in recent years. As inherited from the Lagrangian description of galaxy density evolution, our approach automatically incorporates the non-locality induced by gravitational evolution after the formation of the tracer, and also allows us to include a continuous galaxy formation history by temporally weighted-averaging relevant quantities with the galaxy formation rate.