Studying Galaxy Clusters part2(Astrophysics)
1. Simulation view of galaxy clusters with low X-ray surface brightness(arXiv)
Author : Antonio Ragagnin, Stefano Andreon, Emanuella Puddu
Abstract : X-ray selected samples are known to miss galaxy clusters that are gas poor and have a low surface brightness. This is different for the optically selected samples such as the X-ray Unbiased Selected Sample (XUCS). We characterise the origin of galaxy clusters that are gas poor and have a low surface-brightness by studying covariances between various cluster properties at fixed mass using hydrodynamic cosmological simulations. We extracted approx. 1800 galaxy clusters from a high-resolution Magneticum hydrodynamic cosmological simulation and computed covariances at fixed mass of the following properties: core-excised X-ray luminosity, gas fraction, hot gas temperature, formation redshift, concentration, galaxy richness, fossilness parameter, and stellar mass of the bright central galaxy. We also compared the correlation between concentration and gas fractions in non-radiative simulations, and we followed the trajectories of particles inside galaxy clusters to assess the role of AGN depletion on the gas fraction. In simulations and in observational data, differences in surface brightness are related to differences in gas fraction. Simulations show that the gas fraction strongly correlates with assembly time, in the sense that older clusters are gas poor. Clusters that formed earlier have lower gas fractions because the feedback of the active galactic nucleus ejected a significant amount of gas from the halo. When the X-ray luminosity is corrected for the gas fraction, it shows little or no covariance with other quantities. Older galaxy clusters tend to be gas poor and possess a low X-ray surface brightness because the feedback mechanism removes a significant fraction of gas from these objects. Moreover, we found that most of the LX covariance with the other quantities is explained by differences in the gas fraction.
2. Low redshift calibration of the Amati relation using galaxy clusters(arXiv)
Author : Gowri Govindaraj, Shantanu Desai
Abstract : In this work, we use angular diameter distances of 38 galaxy clusters with joint X-ray/SZE observation to circumvent the circularity problem in the Amati relation for Gamma-ray Bursts (GRBs). Assuming the validity of cosmic-distance duality relation, we obtain the luminosity distance from the cluster angular diameter distance and use that to calculate the isotropic equivalent energy of two different GRB datasets, after restricting the GRB redshift range to z<0.9. We then check the validity of the Amati relation for both these datasets. The best-fit Amati relation parameters using galaxy cluster distances as low-redshift anchors are consistent with a previous estimate for the same dataset. The intrinsic scatter which we obtain for the two datasets is about 45% and 15%, and is comparable with that found by other distance anchors used to vet the Amati relation.
3.Estimating Cosmological Constraints from Galaxy Cluster Abundance using Simulation-Based Inference (arXiv)
Author : Moonzarin Reza, Yuanyuan Zhang, Brian Nord, Jason Poh, Aleksandra Ciprijanovic, Louis Strigari
Abstract : Inferring the values and uncertainties of cosmological parameters in a cosmology model is of paramount importance for modern cosmic observations. In this paper, we use the simulation-based inference (SBI) approach to estimate cosmological constraints from a simplified galaxy cluster observation analysis. Using data generated from the Quijote simulation suite and analytical models, we train a machine learning algorithm to learn the probability function between cosmological parameters and the possible galaxy cluster observables. The posterior distribution of the cosmological parameters at a given observation is then obtained by sampling the predictions from the trained algorithm. Our results show that the SBI method can successfully recover the truth values of the cosmological parameters within the 2σ limit for this simplified galaxy cluster analysis, and acquires similar posterior constraints obtained with a likelihood-based Markov Chain Monte Carlo method, the current state-of the-art method used in similar cosmological studies.
