Related papers: Classification of 4XMM-DR9 Sources by Machine Lear…

Super-soft X-ray sources were established as a heterogeneous class of objects from observations of the Large Magellanic Cloud (LMC). We have searched for new sources of this class in the X-ray images obtained from the XMM-Newton survey of…

Context. The XXL Survey is the largest survey carried out by the XMM-Newton satellite and covers a total area of 50 square degrees distributed over two fields. It primarily aims at investigating the large-scale structures of the Universe…

Context: We present the first Cosmological Parameter inferences from eROSITA X-ray observations of galaxy clusters using a Machine Learning algorithm. Methods: We train a Random Forest using mock catalogs of clusters from Magneticum…

We have processed 2774 high-galactic observations from the XMM archive (as of May 2010) and extracted a serendipitous catalogue of some 850 clusters of galaxies based on purely X-ray criteria, following the methodology developed for the…

We present the results of various automated classification methods, based on machine learning (ML), of objects from data releases 6 and 7 (DR6 and DR7) of the Sloan Digital Sky Survey (SDSS), primarily distinguishing stars from quasars. We…

Reflected or scattered light produce artefacts in astronomical observations that can negatively impact the scientific study. Hence, automated detection of these artefacts is highly beneficial, especially with the increasing amounts of data…

We present the results of a programme to search and identify the nature of unusual sources within the All-sky Wide-field Infrared Survey Explorer (WISE) that is based on a machine-learning algorithm for anomaly detection, namely one-class…

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective at detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced…

We examine three approaches to the problem of source classification in catalogues. Our goal is to determine the confidence with which the elements in these catalogues can be distinguished in populations on the basis of their spectral energy…

The first X-ray survey of the Large Magellanic Cloud (LMC) was performed with the Einstein satellite, revealing diffuse X-ray emission from hot gas and discrete X-ray sources. The ROSAT observations between 1990 and 1998 supplied the most…

With the goal of extracting as much information as possible from Chandra and XMM-Newton observations of faint, diffuse sources such as galaxy clusters, as well as those of future X-ray telescopes, we present a strategy for forward modeling…

We have investigated a number of factors that can have significant impacts on the classification performance of $\gamma$-ray sources detected by Fermi Large Area Telescope (LAT) with machine learning techniques. We show that a framework of…

The Southern Photometric Local Universe Survey (S-PLUS) is a novel project that aims to map the Southern Hemisphere using a twelve filter system, comprising five broad-band SDSS-like filters and seven narrow-band filters optimized for…

The Wide-Area VISTA Extragalactic Survey (WAVES) on the 4-metre Multi-Object Spectroscopic Telescope (4MOST) includes two flux-limited subsurveys with very high (95\%) completeness requirements: Wide over $\sim\!1200$ deg$^2$ and Deep over…

Accurate redshift estimates are a vital component in understanding galaxy evolution and precision cosmology. In this paper, we explore approaches to increase the applicability of machine learning models for photometric redshift estimation…

(abridged:) The XMM-Newton Extended Survey of the Taurus Molecular Cloud (XEST) surveys the most populated ~5 square degrees of the Taurus star formation region, using the XMM-Newton X-ray observatory to study the thermal structure,…

The XMM-Newton Survey Science Center is currently conducting an optical identification programme of serendipitous EPIC sources at low galactic latitudes. The aim of this study is to quantify the various populations contributing to the…

In this paper we present the X-ray analysis of SDSS DR8 redMaPPer (SDSSRM) clusters using data products from the $XMM$ Cluster Survey (XCS). In total, 1189 SDSSRM clusters fall within the $XMM$-Newton footprint. This has yielded 456…

We have applied ClassX, an oblique decision tree classifier optimized for astronomical analysis, to the homogeneous multicolor imaging data base of the Sloan Digital Sky Survey (SDSS), training the software on subsets of SDSS objects whose…

We present a machine learning approach for estimating galaxy cluster masses, trained using both Chandra and eROSITA mock X-ray observations of 2,041 clusters from the Magneticum simulations. We train a random forest regressor, an ensemble…