Cluster Structure in Cosmological Simulations I: Correlation to Observables, Mass Estimates, and Evolution
Tesla E. Jeltema, Eric J. Hallman, Jack O. Burns, and Patrick M. Motl

TL;DR
This study uses cosmological simulations to analyze galaxy cluster structures, their evolution, and how these affect mass estimates and observable relations, highlighting the importance of structure-based corrections for accurate cosmological measurements.
Contribution
It demonstrates that cluster structure measures can correct hydrostatic mass biases, improving the accuracy of mass-observable relations in cosmological studies.
Findings
Simulated cluster structures match low-redshift observations well.
Hydrostatic mass estimates are systematically biased low, especially in disturbed clusters.
Cluster structure evolution with redshift can impact high-redshift cosmological analyses.
Abstract
We use Enzo, a hybrid Eulerian AMR/N-body code including non-gravitational heating and cooling, to explore the morphology of the X-ray gas in clusters of galaxies and its evolution in current generation cosmological simulations. We employ and compare two observationally motivated structure measures: power ratios and centroid shift. Overall, the structure of our simulated clusters compares remarkably well to low-redshift observations, although some differences remain that may point to incomplete gas physics. We find no dependence on cluster structure in the mass-observable scaling relations, T_X-M and Y_X-M, when using the true cluster masses. However, estimates of the total mass based on the assumption of hydrostatic equilibrium, as assumed in observational studies, are systematically low. We show that the hydrostatic mass bias strongly correlates with cluster structure and, more…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
