Related papers: CHEX-MATE: A non-parametric deep learning techniqu…
Recently, surrogate models based on deep learning have attracted much attention for engineering analysis and optimization. As the construction of data pairs in most engineering problems is time-consuming, data acquisition is becoming the…
In the outskirts of galaxy clusters, entropy profiles measured from X-ray observations of the hot intracluster medium (ICM) drops off unexpectedly. One possible explanation for this effect is gas clumping, where pockets of cooler and denser…
Using a deprojection technique, we study the X-ray properties of the galaxy cluster Abell 1835 observed with Chandra, including temperature, abundance, electron density, gas mass fraction, and total mass. A comparison with the results…
We develop a machine learning algorithm to infer the 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel'dovich effect maps. We generate around 73,000 mock images along various lines of sight…
We present results from a joint X-ray/Sunyaev-Zel'dovich modeling of the intra-cluster gas using XMM-Newton and APEX-SZ imaging data. The goal is to study the physical properties of the intra-cluster gas with a non-parametric de-projection…
We examine a Geometric Deep Learning model as a thermodynamic system treating the weights as non-quantum and non-relativistic particles. We employ the notion of temperature previously defined in [7] and study it in the various layers for…
We characterise the entropy profiles of 32 very high mass ($M_{500}>7.75\times10^{14}~M_{\odot}$) galaxy clusters (HIGHMz), selected from the CHEX-MATE sample, to study the intracluster medium (ICM) entropy distribution in a regime where…
Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new frontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of…
Theoretical studies of the physical processes guiding the formation and evolution of galaxies and galaxy clusters in the X-ray are mainly based on the results of numerical hydrodynamical N-body simulations, which in turn are often directly…
The representation of nonlinear sub-grid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but…
We explore the use of deep neural networks for nonlinear dimensionality reduction in climate applications. We train convolutional autoencoders (CAEs) to encode two temperature field datasets from pre-industrial control runs in the CMIP5…
We introduce Deep-CEE (Deep Learning for Galaxy Cluster Extraction and Evaluation), a proof of concept for a novel deep learning technique, applied directly to wide-field colour imaging to search for galaxy clusters, without the need for…
We present a new model for the X-ray properties of the intracluster medium that explicitly includes heating of the gas by the energy released during the evolution of cluster galaxies. We calculate the evolution of clusters by combining the…
Automated construction of surface geometries of cardiac structures from volumetric medical images is important for a number of clinical applications. While deep-learning-based approaches have demonstrated promising reconstruction precision,…
A variety of physical heating mechanisms are combined with radiative cooling to explore, via one dimensional hydrodynamic simulations, the expected thermal properties of the intracluster medium (ICM) in the context of the cooling flow…
The analysis of X-ray and Sunyaev-Zel\'dovich measurements of the intracluster medium (ICM) assumes that electrons are in thermal equilibrium with ions in the plasma. However, electron-ion equilibration timescales can be comparable to the…
Numerous ongoing and future large area surveys (e.g. DES, EUCLID, LSST, WFIRST), will increase by several orders of magnitude the volume of data that can be exploited for galaxy morphology studies. The full potential of these surveys can…
Thermal issue is a major concern in 3D integrated circuit (IC) design. Thermal optimization of 3D IC often requires massive expensive PDE simulations. Neural network-based thermal prediction models can perform real-time prediction for many…
Thermal conduction in the intracluster medium has been proposed as a possible heating mechanism for offsetting central cooling losses in rich clusters of galaxies. In this study, we introduce a new formalism to model conduction in a diffuse…
The failure of purely gravitational models of X-ray cluster formation to reproduce basic observed properties of the local cluster population suggests the need for one or more additional physical processes operating on the intracluster…