Related papers: CHEX-MATE: A non-parametric deep learning techniqu…
We propose a novel component to the understanding of the temperature structure of galaxy clusters which does not rely on any heating or cooling mechanism. The new ingredient is the use of non-extensive thermo-statistics which is based on…
In this paper we describe a self-contained method for performing the spectral-imaging deconvolution of X-ray data on clusters of galaxies observed by the ASCA satellite. Spatially-resolved spectral studies of data from this satellite…
Simulation is crucial for all aspects of collider data analysis, but the available computing budget in the High Luminosity LHC era will be severely constrained. Generative machine learning models may act as surrogates to replace…
We constrain gas and dark matter (DM) parameters of galaxy groups and clusters, by comparing X-ray scaling relations to theoretical expectations, obtained assuming that the gas is in hydrostatic equilibrium with the DM and follows a…
We present preliminary results from an on-going program that aims at mapping the intracluster medium (ICM) temperature of high redshift galaxy clusters from the MaDCoWS sample using a joint analysis of shallow X-ray data obtained by…
We use a sample of 62 clusters of galaxies to investigate the discrepancies of gas temperature and total mass within r500 between XMM-Newton and Chandra data. Comparisons of the properties show that: (1) Both the de-projected and projected…
We use high-resolution hydrodynamic simulations to investigate the density profile of hot gas in clusters of galaxies, adopting a variant of cold dark matter cosmologies and employing a cosmological N-body/smoothed particle hydrodynamics…
Using a set of hydrodynamical simulations of 9 galaxy clusters with masses in the range 1.5 10^{14} M_sun < M_vir < 3.4 10^{15} M_sun, we have studied the density, temperature and X-ray surface brightness profiles of the intracluster medium…
We study the total gravitating mass distribution in the central region of 23 clusters of galaxies with Chandra. Using a new deprojection technique, we measure the temperature and gas density in the very central region of the clusters as a…
We introduce the cosmological HYPER code based on an innovative hydro-particle-mesh (HPM) algorithm for efficient and rapid simulations of gas and dark matter. For the HPM algorithm, we update the approach of Gnedin & Hui (1998) to expand…
Cosmological constraints from clusters rely on accurate gravitational mass estimates, which strongly depend on cluster gas temperature measurements. Therefore, systematic calibration differences may result in biased, instrument-dependent…
While clusters of galaxies are regarded as one of the most important cosmological probes, the conventional spherical modeling of the intracluster medium (ICM) and the dark matter (DM), and the assumption of strict hydrostatic equilibrium…
We propose a network for semantic mapping called the Dense Dilated Convolutions Merging Network (DDCM-Net) to provide a deep learning approach that can recognize multi-scale and complex shaped objects with similar color and textures, such…
Over the past several years, there have been many studies demonstrating the ability of deep neural networks to identify phase transitions in many physical systems, notably in classical statistical physics systems. One often finds that the…
We study the evolution of the ICM with a sample of 70 galaxy clusters spanning 0.18 < z < 1.24. We find that X-ray luminosity and ICM mass at a fixed temperature evolve with redshift in a manner inconsistent with the standard self-similar…
In this document, a pileup deconvolution scheme not relying on any mathematics guessing is presented. In high energy physics experiment, as the luminosity increases, pile-up issues on detectors such as calorimeters become non-negligible.…
XMM-Newton, with its high throughput and excellent spatial and spectral resolution, is an ideal instrument for spectro-imaging observation of clusters. Presented here is an XMM-Newton mosaic observation of A2163, from which a new radial…
In order to investigate the spatial distribution of the ICM temperature in galaxy clusters in a quantitative way and probe the physics behind, we analyze the X-ray spectra of a sample of 50 galaxy clusters, which were observed with the…
This work proposes an unsupervised fusion framework based on deep convolutional transform learning. The great learning ability of convolutional filters for data analysis is well acknowledged. The success of convolutive features owes to…
We propose an ambitious new method that models the intracluster medium in clusters of galaxies as a set of X-ray emitting smoothed particles of plasma. Each smoothed particle is described by a handful of parameters including temperature,…