Luminosity-Temperature Relation as a Probe for Modified Gravity

TL;DR

This study explores how the luminosity-temperature relation of galaxy clusters can serve as a robust test for modified gravity theories, especially at low-mass scales where deviations are most pronounced.

Contribution

It introduces an improved semi-analytic framework incorporating key physical effects to distinguish modified gravity models from standard cosmology using X-ray cluster data.

Findings

Massive clusters follow standard predictions due to screening.

Low-mass systems show steeper $L$-$T$ slopes in MG scenarios.

MG models fit observational data better than $ ull$Lambda$ ull$CDM.

Abstract

We investigate the luminosity-temperature ($L$-$T$) relation of galaxy clusters as a probe for testing modified gravity (MG) theories, focusing on $f(R)$ gravity and symmetron models. Using an improved semi-analytic framework that incorporates angular momentum acquisition, dynamical friction, and shock heating within the modified punctuated equilibrium model, we compare predictions against hydrodynamical simulations and observational data. While massive clusters remain largely screened and follow standard $\Lambda$CDM predictions, low-mass systems ($kT \lesssim 1-2$ keV) exhibit systematic deviations characterized by steeper $L$-$T$ slopes in MG scenarios. Crucially, we demonstrate that these signatures cannot be mimicked by conventional astrophysical processes such as feedback or angular momentum effects, which primarily affect normalization rather than curvature. Our results establish the $L$-$T$ relation as a robust diagnostic tool for distinguishing general relativity from screened MG theories, with the strongest discriminatory power emerging at group scales accessible to current and future X-ray surveys. Moreover, a normalized reduced $\chi^2$ analysis of the $L$-$T$ relation shows that MG models provide significantly better agreement with observational data than $\Lambda$CDM, with several realizations achieving excellent fits while the $\Lambda$CDM model consistently performs worst.