Related papers: Classifying Unidentified X-ray Sources in the Chan…

Following the presentation of the XMM-LSS X-ray source detection package by Pacaud et al., we provide the source lists for the first 5.5 surveyed square degrees. The catalogues pertain to the [0.5-2] and [2-10] keV bands and contain in…

We present followup optical g', r', and i', imaging and spectroscopy of serendipitous X-ray sources detected in 6 archival Chandra, images included in the Chandra, Multiwavelength Project (ChaMP). Of the 486 X-ray sources detected between…

[Abridged] The central field of the Andromeda galaxy (M 31) has been monitored, using the Chandra HRC-I detector (about 0.1-10 keV energy range) from 2006 to 2012 with the main aim to detect X-rays from optical novae. We present a…

XMM-Newton provides unprecedented insight into the X-ray Universe, recording variability information for hundreds of thousands of sources. Manually searching for interesting patterns in light curves is impractical, requiring an automated…

We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…

With growing data volumes from synoptic surveys, astronomers must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that…

We propose a machine-learning-based technique to determine the number density of radio sources as a function of their flux density, for use in next-generation radio surveys. The method uses a convolutional neural network trained on…

We present an updated catalog of X-ray point sources in the inner 500$\arcsec$ ($\sim$20 parsec) of the Galactic Center (GC), where the {\it nuclear star cluster} (NSC) stands, based on a total of $\sim$4.5 Ms of {\it Chandra} observations…

Machine learning (ML) offers a collection of powerful approaches for detecting and modeling associations, often applied to data having a large number of features and/or complex associations. Currently, there are many tools to facilitate…

Upcoming synoptic surveys are set to generate an unprecedented amount of data. This requires an automatic framework that can quickly and efficiently provide classification labels for several new object classification challenges. Using data…

Material scientists are increasingly adopting the use of machine learning (ML) for making potentially important decisions, such as, discovery, development, optimization, synthesis and characterization of materials. However, despite ML's…

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…

It is assumed that the unresolved fraction of the X-ray background (XRB) consists of a truly diffuse component and a population of the weak sources below the present detection threshold. Albeit these weak sources are not observed directly,…

We present X-ray source catalogs for the $\approx7$ Ms exposure of the Chandra Deep Field-South (CDF-S), which covers a total area of 484.2 arcmin$^2$. Utilizing WAVDETECT for initial source detection and ACIS Extract for photometric…

We present results of a Chandra survey of the ultra-luminous X-ray sources (ULX) in 13 normal galaxies, in which we combine source detection with X-ray flux measurement. 22 ULX were detected, i.e. with L_x > 1 x 10^{39} erg s^{-1} (L_10),…

With ~2 Ms of Chandra exposure, the Chandra Deep Field-North (CDF-N) survey provides the deepest view of the Universe in the 0.5-8.0 keV band. Five hundred and three (503) X-ray sources are detected down to on-axis 0.5-2.0 keV and 2-8 keV…

The Chandra data archive after eight years' accumulation is a treasure for various studies, and in this paper we exploit this valuable resource to study the X-ray point source populations in nearby galaxies. By December 14, 2007, 383…

We have detected 18 sources over 6 sigma threshold within two regions 8.3X16.9 arcmin^2 and 8.3X33.6 arcmin^2 in the vicinity of the point with alpha=03h31m02.45s (J2000) and delta=+43degree47arcmin58.5arcsec (J2000) using a CHANDRA ACIS…

The study of machine learning (ML) techniques for the autonomous classification of astrophysical sources is of great interest, and we explore its applications in the context of a multifrequency data-frame. We test the use of supervised ML…

We present an analysis of two deep (75 ks) Chandra observations of the European Large Area ISO Survey (ELAIS) fields N1 and N2 as the first results from the ELAIS deep X-ray survey. This survey is being conducted in well studied regions…