Related papers: Automatic classification of time-variable X-ray so…
ClassX is a project aimed at creating an automated system to classify X-ray sources and is envisaged as a prototype of the Virtual Observatory. As a system, ClassX integrates into a pipeline a network of classifiers and an engine that…
Extensive astronomical surveys, like those conducted with the {\em Chandra} X-ray Observatory, detect hundreds of thousands of unidentified cosmic sources. Machine learning (ML) methods offer an efficient, probabilistic approach to classify…
Despite the growing number of gamma-ray sources detected by Fermi-LAT, about one third of the sources in each survey remains of uncertain type. We present a new deep neural network approach for the classification of unidentified or…
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,…
We carry out classification of 4330 X-ray sources in the 2XMMi-DR3 catalog. They are selected under the requirement of being a point source with multiple XMM-Newton observations and at least one detection with the signal-to-noise ratio…
The EXTraS project, based on data collected with the XMM-Newton observatory, provided us with a vast amount of light curves for X-ray sources. For each light curve, EXTraS also provided us with a set of features (https://extras.inaf.it). We…
Classification of sources is one of the most important tasks in astronomy. Sources detected in one wavelength band, for example using gamma rays, may have several possible associations in other wavebands, or there may be no plausible…
We are entering an era of unprecedented quantities of data from current and planned survey telescopes. To maximise the potential of such surveys, automated data analysis techniques are required. Here we implement a new methodology for…
We present photometric redshifts for 1,031 X-ray sources in the X-ATLAS field, using the machine learning technique TPZ (Carrasco Kind & Brunner 2013). X-ATLAS covers 7.1 deg2 observed with the XMM-Newton within the Science Demonstration…
Every field of Science is undergoing unprecedented changes in the discovery process, and Astronomy has been a main player in this transition since the beginning. The ongoing and future large and complex multi-messenger sky surveys impose a…
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…
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…
Aims: Pointed observations with XMM-Newton provide the basis for creating catalogues of X-ray sources detected serendipitously in each field. This paper describes the creation and characteristics of the 2XMM catalogue. Methods: The 2XMM…
We describe the use of an unsupervised Random Forest for similarity learning and improved unsupervised anomaly detection. By training a Random Forest to discriminate between real data and synthetic data sampled from a uniform distribution…
Galaxy clusters appear as extended sources in XMM-Newton images, but not all extended sources are clusters. So, their proper classification requires visual inspection with optical images, which is a slow process with biases that are almost…
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…
We have undertaken a dedicated program of automatic source classification in the WISE database merged with SuperCOSMOS scans, comprehensively identifying galaxies, quasars and stars on most of the unconfused sky. We use the Support Vector…
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…
Since its launch in 1999, the XMM-\textit{Newton} mission has compiled the largest catalogue of serendipitous X-ray sources, with the 3XMM being the third version of this catalogue. This is because of the combination of a large effective…
Automatic source detection and classification tools based on machine learning (ML) algorithms are growing in popularity due to their efficiency when dealing with large amounts of data simultaneously and their ability to work in…