# Probing galaxy cluster and intra-cluster gas with luminous red galaxies

## TL;DR

This study uses cross-correlation of Planck tSZ data and SDSS LRGs across different redshifts to analyze galaxy cluster gas properties, revealing evolution in pressure profile parameters and suggesting modifications to the universal pressure profile model.

## Contribution

It introduces a redshift-dependent modification to the universal pressure profile to better fit tSZ-LRG cross-correlation data across multiple redshift bins.

## Key findings

- Best-fit UPP parameters are consistent with previous studies.
- Original UPP model struggles at small scales in some redshift bins.
- Including a redshift-dependence parameter improves model fit.

## Abstract

We use the cross-correlation between the thermal Sunyaev-Zeldovich (tSZ) signal measured by the Planck satellite and the luminous red galaxy (LRG) samples provided by the SDSS DR7 to study the properties of galaxy cluster and intra-cluster gas. We separate the samples into three redshift bins z_1=(0.16, 0.26), z_2=(0.26, 0.36), z_3=(0.36, 0.47), and stack the Planck y-map against LRGs to derive the averaged y-profile for each redshift bin. We then fit the stacked profile with the theoretical prediction from the universal pressure profile (UPP) by using the Markov-Chain Monte-Carlo method. We find that the best-fit values of the UPP parameters for the three bins are generally consistent with the previous studies, except for the noticeable evolution of the parameters in the three redshift bins. We simultaneously fit the data in the three redshift bins together, and find that the original UPP model cannot fit the data at small angular scales very well in the first and third redshift bins. The joint fits can be improved by including an additional parameter eta to change the redshift-dependence of the model (i.e. E(z)^{8/3} -> E(z)^{8/3+eta}) with best-fit value as eta=-3.11^{+1.09}_{-1.13}. This suggests that the original UPP model with less redshift-dependence may provide a better fit to the stacked thermal Sunyaev-Zeldovich profile.