Luminosity Function of Galaxies in Voids: A Modification Inspired by Excursion Set Theory

TL;DR

This paper develops a modified galaxy luminosity function model for void regions using excursion set theory, incorporating environmental and redshift effects, and validates it with observational data showing improved fit over previous models.

Contribution

Introduces a new model for the galaxy luminosity function in voids based on excursion set theory, accounting for environment and redshift dependence, with validation against observational data.

Findings

Model effectively fits galaxy luminosity functions in voids.

Incorporating environment and redshift improves model accuracy.

Model outperforms previous models based on BIC and AIC criteria.

Abstract

In the standard picture of cosmology, the galaxies reside in dark matter (DM) halos. DM halos are distributed in the cosmic web in different environments. The luminosity of the galaxies in different environments can be used as a probe to assess a cosmological model. This study focuses on the properties of galaxies in void regions, where halos typically do not experience extreme conditions. By examining the galaxy luminosity function, we aim to understand the dependence of galaxy properties on their environment and redshift so that later, we can use this as a tool to evaluate cosmological models. We employ the excursion set theory to incorporate parameters related to the number density of DM halos into the luminosity function. Using the Galaxy and Mass Assembly (GAMA) survey and 2dFGRS datasets, we fit our theoretical models to observational data, examining the environmental and redshift dependence of the galaxy luminosity function. Our results indicate that we model the galaxy luminosity function in voids effectively by considering the linear density contrast of the environment and the growth function $D(z)$ for redshift dependence. This study provides a model for the environmental dependence of galaxy luminosity function that offers an improvement in the $\chi ^2$ parameter compared to the previously proposed model in \cite{mcnaught2014galaxy}. Both Bayesian information criterion (BIC) and Akaike information criterion (AIC) tests support the superiority of this model for the void region.