M. Lawrence Pattersons, Feri Apryandi, Freddy P. Zen
Galaxy clusters are the largest virialized structures in the Universe and are predominantly dominated by dark matter. The hydrostatic mass and the mass obtained from gravitational lensing measurements generally differ, a discrepancy known as the hydrostatic mass bias. In this work, we derive the hydrostatic mass of galaxy clusters within the framework of Rastall gravity. We consider two scenarios: (i) the absence of dark matter and (ii) the presence of dark matter. In both cases, we constrain the Rastall parameter in the cluster-scale using observational data. In the first scenario, Rastall gravity effectively reduces the hydrostatic mass, bringing it closer to the observed baryonic mass. The best linear fit yields a slope M=1.07±0.11, indicating a near one-to-one correspondence between the two masses. In the second scenario, Rastall gravity helps to alleviate the hydrostatic mass bias. The linear fit between the Rastall hydrostatic mass and the observed lensing mass results in a best-fit slope M=0.99±0.26, which is very close to unity. We also calculate the goodness-of-fit for every fit. The statistical evaluations indicate that Rastall gravity provides a viable phenomenological framework that can improve certain aspects of the mass discrepancy problem at the level of scaling relations. However, it does not universally outperform other modified gravity model, when evaluated using standard goodness-of-fit criteria. © 2026 The Authors.
Theoretical High Energy Physics Group, Department of Physics, Institut Teknologi Bandung, Jl. Ganesha 10, Bandung, 40132, Indonesia; Indonesia Center for Theoretical and Mathematical Physics (ICTMP), Institut Teknologi Bandung, Jl. Ganesha 10, Bandung, 40132, Indonesia; Physics Study Program, Faculty of Mathematics and Natural Science Education, Universitas Pendidikan Indonesia, Bandung, Indonesia; Research Group on Environmental Exploration, Mitigation, Education, and Defense of Earth & Outer Space, Universitas Pendidikan Indonesia, Bandung, Indonesia; Learning Analytics and Digital Assessment Research Group, Universitas Pendidikan Indonesia, Bandung, Indonesia; Center of Excellence Astronomical Data Science and Light Pollution, Universitas Pendidikan Indonesia, Bandung, Indonesia
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