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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…
To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of…
We report the results from an homogeneous analysis of the X-ray (Chandra-ACIS) data available for a sample of 51 LINER galaxies selected from the catalogue by Carrillo et al. (1999) and representative of the population of bright LINER…
The detection of new clusters of galaxies or the study of known clusters of galaxies in X-rays can be complicated by the presence of X-ray point sources, the majority of which will be active galactic nuclei (AGN). This can be addressed by…
Machine learning (ML) and Deep Learning (DL) methods are being adopted rapidly, especially in computer network security, such as fraud detection, network anomaly detection, intrusion detection, and much more. However, the lack of…
The allure of aesthetic appeal in images captivates our senses, yet the underlying intricacies of aesthetic preferences remain elusive. In this study, we pioneer a novel perspective by utilizing several different machine learning (ML)…
This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad line), quasars, and stars. We…
We present a collection of classified X-ray sources in Globular Clusters (GCs) observed by the Chandra X-ray Observatory (CXO), including active binaries, cataclysmic variables, millisecond pulsars, and low-mass X-ray binaries. We…
With the superb angular resolution of the Chandra Observatory, it is now possible to detect X-ray point sources, either embedded in galaxy clusters or along the cluster line of sight, which could not be resolved by previous instruments.…
Within the framework of our program of assessment of the nature of unidentified or poorly known INTEGRAL sources, we present here spectroscopy of optical objects, selected through positional cross-correlation with soft X-ray detections…
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…
We present Chandra and XMM observations of 12 bright (f(2-10 keV) > 10^-13 cgs) sources from the ASCA SHEEP (Search for the High Energy Extragalactic Population) survey. Most of these have been either not observed or not detected previously…
Machine learning based approaches are emerging as very powerful tools for many applications including source classification in astrophysics research due to the availability of huge high quality data from different surveys in observational…
We search for unresolved X-ray emission from lensed sources in the FOV of 11 CLASH clusters with Chandra data. We consider the solid angle in the lens plane corresponding to a magnification $\mu>1.5$, that amounts to a total of ~100…
Improving the capabilities of detecting faint X-ray sources is fundamental to increase the statistics on faint high-z AGN and star-forming galaxies. We performed a simultaneous Maximum Likelihood PSF fit in the [0.5-2] keV and [2-7] keV…
(Abridged) Since its launch in October 2002, the INTEGRAL satellite has revolutionized our knowledge of the hard X-ray sky thanks to its unprecedented imaging capabilities and source detection positional accuracy above 20 keV. Nevertheless,…
The Chandra Multiwavelength Plane (ChaMPlane) Survey is a comprehensive effort to constrain the population of accretion-powered and coronal low-luminosity X-ray sources (L_X < 10^33 erg s^-1) in the Galaxy. ChaMPlane incorporates X-ray,…
We present a new method for determining the sensitivity of X-ray imaging observations, which correctly accounts for the observational biases that affect the probability of detecting a source of a given X-ray flux, without the need to…
We present the Chandra Multiwavelength Project (ChaMP) X-ray point source catalog with ~6,800 X-ray sources detected in 149 Chandra observations covering \~10 deg^2. The full ChaMP catalog sample is seven times larger than the initial…
We present a simple but informative online tool to visualize the multiwavelength properties of about 2,700 X-ray sources from Chandra Source Catalog version 2.0 with literature verified classifications. Here we describe the catalogs that we…