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Extended stellar haloes are a natural by-product of the hierarchical formation of massive galaxies. If merging is a non-negligible factor in the growth of our Galaxy, evidence of such events should be encoded in its stellar halo. Reliable…

Solar flares are intense eruptive events caused by the rapid release of magnetic energy, often impacting Earth's space environment through electromagnetic radiation and high-energy particles. Accurate flare prediction is critical for space…

Top tagging has emerged as a fast-evolving subject due to the top quark's significant role in probing physics beyond the standard model. For the reconstruction of top jets, machine learning models have shown a substantial improvement in the…

There is a large body of work that has used the excellent Chandra observations of nearby galaxies with neglible low mass X-ray binary (LMXB) populations. This has culminated in a ``Universal'' X-ray luminosity function (XLF) for high mass…

Anomaly Detection (AD) focuses on identifying unusual behaviors in complex datasets. Machine Learning (ML) algorithms and Decision Support Systems (DSSs) provide effective solutions for AD, but detecting anomalies alone may not be enough,…

We present an analysis of the X-ray spectral properties of 61 hard X-ray selected (2-8 keV) sources from the bright ($f_{2-8 keV}>10^{-14}$ \funits) {\it XMM-Newton}/2dF survey. This comprises of 9 {\it XMM-Newton} pointings in the North…

We present XMM-Newton observations of a representative X-ray selected sample of 31 galaxy clusters at moderate redshift $(0.4<z<0.6)$, spanning the mass range $10^{14} < M_{\textrm 500} < 10^{15}$~M$_\odot$. This sample, EXCPRES (Evolution…

In this paper, a review is given of methods useful for XMM-Newton EPIC data analysis of extended sources, along with some applications to a bright massive cluster of galaxies at z=0.2, Abell 209. This may constitute an introduction to that…

AI explainability improves the transparency of models, making them more trustworthy. Such goals are motivated by the emergence of deep learning models, which are obscure by nature; even in the domain of images, where deep learning has…

We used a supervised machine learning algorithm (probabilistic random forest) to classify ~130 million sources in the VISTA Survey of the Magellanic Clouds (VMC). We used multi-wavelength photometry from optical to far-infrared as features…

We introduce a method for extracting spectral information from energy-resolved light curves folded at the neutron star spin period (known as pulse profiles) in accreting X-ray binaries. Spectra of these sources are sometimes characterized…

We present a novel multimodal neural network (MNN) for classifying astronomical sources in multiband ground-based observations, from optical to near infrared, to separate sources in stars, galaxies and quasars. Our approach combines a…

We present deep Swift follow-up observations of a sample of 94 unidentified X-ray sources from the XMM-Newton Slew Survey. The X-ray Telescope on-board Swift detected 29% of the sample sources; the flux limits for undetected sources…

The study of the attribution of input features to the output of neural network models is an active area of research. While numerous Explainable AI (XAI) techniques have been proposed to interpret these models, the systematic and automated…

Classifying variable stars is key for understanding stellar evolution and galactic dynamics. With the demands of large astronomical surveys, machine learning models, especially attention-based neural networks, have become the…

We present the results of an automated variability analysis of the Kepler public data measured in the first quarter (Q1) of the mission. In total, about 150 000 light curves have been analysed to detect stellar variability, and to identify…

We present a new method to identify luminous off-nuclear X-ray sources in the outskirts of galaxies from large public redshift surveys, distinguishing them from foreground and background interlopers. Using the 3XMM-DR5 catalog of X-ray…

The high-resolution X-ray spectroscopy made possible by the 1999 deployment of the Chandra X-ray Observatory has revolutionized our understanding of stellar X-ray emission. Many puzzles remain, though, particularly regarding the mechanisms…

X-ray extragalactic surveys are ideal laboratories for the study of the evolution and clustering of active galactic nuclei (AGN). The XXL Survey spans two fields of a combined 50 $deg^2$ observed for more than 6Ms with XMM-Newton, occupying…

Power density spectra (PDS) that are characteristic of low mass X-ray binaries (LMXBs) have been previously reported for M31 X-ray sources observed by XMM-Newton. However, we have recently discovered that these PDS are false positives…