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The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the…
Context. Distant galaxy clusters provide an effective laboratory in which to study galaxy evolution in dense environments and at early cosmic times. Aims. We aim to identify distant galaxy clusters as extended X-ray sources coincident with…
This study investigates the effects of including patients' clinical information on the performance of deep learning (DL) classifiers for disease location in chest X-ray images. Although current classifiers achieve high performance using…
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…
Weak lensing applied to deep optical images of clusters of galaxies provides a powerful tool to reconstruct the distribution of the gravitating mass associated to these structures. We use the shear signal extracted by an analysis of deep…
Traditional weak-lensing mass reconstruction techniques suffer from various artifacts, including noise amplification and the mass-sheet degeneracy. In Hong et al. (2021), we demonstrated that many of these pitfalls of traditional mass…
(Abridged) Galaxy clusters are a powerful probe of cosmological models. Next generation large-scale optical and infrared surveys will reach unprecedented depths over large areas and require highly complete and pure cluster catalogs, with a…
Galaxy clusters are powerful probes of astrophysics and cosmology through gravitational lensing: the clusters' mass, dominated by 85% dark matter, distorts background light. Yet, mass reconstruction lacks the scalability and large-scale…
We present a very large high-resolution cosmological N-body simulation, the Millennium-XXL or MXXL, which uses 303 billion particles to represent the formation of dark matter structures throughout a 4.1Gpc box in a LambdaCDM cosmology. We…
Purpose: To evaluate the quality of deep learning reconstruction for prospectively accelerated intraoperative magnetic resonance imaging (iMRI) during resective brain tumor surgery. Materials and Methods: Accelerated iMRI was performed…
We perform a comprehensive study of the total mass distribution of the galaxy cluster RXCJ2248 ($z=0.348$) with a set of high-precision strong lensing models, which take advantage of extensive spectroscopic information on many multiply…
Deep Learning (DL) , a variant of the neural network algorithms originally proposed in the 1980s, has made surprising progress in Artificial Intelligence (AI), ranging from language translation, protein folding, autonomous cars, and more…
Context: The shape of the mass density profiles of cosmological halos informs us of the nature of DM and DM-baryons interactions. Previous estimates of the inner slope of the mass density profiles of clusters of galaxies are in opposition…
Using a non-parametric procedure developed by Kaiser \& Squires (1993), we analyse the statistical image distortions of faint field galaxies to $I\ls25.5$ in two distant X-ray luminous clusters 1455+22 ($z_{cl}=0.26$) and 0016+16…
We present a new non-parametric method for determining mean 3D density and mass profiles from weak lensing measurements around stacked samples of galaxies or clusters, that is, from measurement of the galaxy-shear or cluster-shear…
We present a comprehensive analysis of strong-lensing, weak-lensing shear and magnification data for a sample of 16 X-ray-regular and 4 high-magnification galaxy clusters selected from the CLASH survey. Our analysis combines constraints…
The first building block to use galaxy clusters in astrophysics and cosmology is the accurate determination of their mass. Two of the most well regarded direct mass estimators are based on weak lensing (WL) determinations or X-ray analyses…
We present a set-based machine learning framework that infers posterior distributions of galaxy cluster masses from projected galaxy dynamics. Our model combines Deep Sets and conditional normalizing flows to incorporate both positional and…
We present new mass estimates of the galaxy cluster MS2137.3-2353, inferred from X-ray and strong lensing analyses. This cluster exhibits an outstanding strong lensing configuration and indicates a well-relaxed dynamical state, being most…
Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any…