Related papers: Exploring X-ray variability with unsupervised mach…
We present variability analysis of data from the Northern Sky Variability Survey (NSVS). Using the clustering method which defines variable candidates as outliers from large clusters, we cluster 16,189,040 light curves, having data points…
A procedure is described for estimating an optimum kernel for the detection by convolution of signals among Poissonian noise. The technique is applied to the detection of x-ray point sources in XMM-Newton data, and is shown to yield an…
We present the global X-ray properties of the point source population in the grand-design spiral galaxy M101, as seen with XMM-Newton. 108 X-ray sources are detected within the D25 ellipse of M101, of which ~24 are estimated to be…
Time-Spatial data plays a crucial role for different fields such as traffic management. These data can be collected via devices such as surveillance sensors or tracking systems. However, how to efficiently an- alyze and visualize these data…
SOM is a type of unsupervised learning where the goal is to discover some underlying structure of the data. In this paper, a new extraction method based on the main idea of Concurrent Self-Organizing Maps (CSOM), representing a…
The COSMOS survey is a multiwavelength survey aimed to study the evolution of galaxies, AGN and the large scale structure. The XMM-COSMOS is a deep X-ray survey over the full 2 deg2 of the COSMOS area. It consists of 55 XMM-Newton pointings…
We present an X-ray point-source catalog from the XMM-Large Scale Structure survey region (XMM-LSS), one of the XMM-Spitzer Extragalactic Representative Volume Survey (XMM-SERVS) fields. We target the XMM-LSS region with $1.3$ Ms of new…
Modern time-domain surveys continuously monitor large swaths of the sky to look for astronomical variability. Astrophysical discovery in such data sets is complicated by the fact that detections of real transient and variable sources are…
This paper defines a new learning architecture, Layered Self-Organizing Maps (LSOMs), that uses the SOM and supervised-SOM learning algorithms. The architecture is validated with the MNIST database of hand-written digit images. LSOMs are…
In numerous applicative contexts, data are too rich and too complex to be represented by numerical vectors. A general approach to extend machine learning and data mining techniques to such data is to really on a dissimilarity or on a kernel…
The current and upcoming large data volume galaxy surveys require the use of machine learning techniques to maximize their scientific return. This study explores the use of Self-Organizing Maps (SOMs) to estimate galaxy parameters with a…
Sky surveys produce enormous quantities of data on extensive regions of the sky. The easiest way to access this information is through catalogues of standardised data products. {\em XMM-Newton} has been surveying the sky in the X-ray,…
One of the major and unfortunately unforeseen sources of background for the current generation of X-ray telescopes are few tens to hundreds of keV (soft) protons concentrated by the mirrors. One such telescope is the European Space Agency's…
Context. Serendipitous X-ray surveys have proven to be an efficient way to find rare objects, for example tidal disruption events, changing-look active galactic nuclei (AGN), binary quasars, ultraluminous X-ray sources (ULXs), and…
The study refers to the application of a type of artificial neural network called the Self-Organising Map (SOM) for the identification of areas of the human abdominal wall that perform in a similar mechanical way. The research was based on…
In this study we revisit public data on the supersoft X-ray source CAL 83 in the Large Magellanic Cloud. A significant part of our analysis is focused on XMM-Newton X-ray observations, in which updated data reduction procedures and quality…
The interpretation of ligand-target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit…
An XMM-Newton study of ultraluminous X-ray sources (ULX) has been performed in a sample of 10 nearby Seyfert galaxies. Eighteen ULX have been found with positional uncertainty of about 4". The large collecting area of XMM-Newton makes the…
We report on the production of a large area, shallow, sky survey, from XMM-Newton slews. The great collecting area of the mirrors coupled with the high quantum efficiency of the EPIC detectors have made XMM-Newton the most sensitive X-ray…
We present an analysis of 7 clusters observed by XMM as part of our survey of 17 most X-ray luminous clusters of galaxies at z=0.2 selected for a comprehensive and unbiased study of the mass distribution in massive clusters. Using public…