相关论文: Explainable machine learning classification of \te…
Hard X-ray surveys like those provided by IBIS and BAT on board the INTEGRAL and Swift satellites list a significant number of sources which are unidentified and/or unclassified and which deserve multiwaveband observations to be properly…
We introduce the New-ANGELS program, an XMM-Newton survey of $\sim7.2\rm~deg^2$ area around M 31, which aims to study the X-ray populations in M 31 disk and the X-ray emitting hot gas in the inner halo of M 31 up to 30 kpc. In this first…
Detecting anomalies in energy consumption data is crucial for identifying energy waste, equipment malfunction, and overall, for ensuring efficient energy management. Machine learning, and specifically deep learning approaches, have been…
We report on our search for distant clusters of galaxies based on optical and X-ray follow up observations of X-ray candidates from the SHARC survey. Based on the assumption that the absence of bright optical or radio counterparts to…
We present results from our X-ray analysis of the first systematic search for triple AGN in nearby (z<0.077) triple galaxy mergers. We analyze archival Chandra observations of 7 triple galaxy mergers with BAYMAX (Bayesian Analysis of…
We present a catalog of cross-correlated radio, infrared and X-ray sources using a very restrictive selection criteria with an IDL-based code developed by us. The significance of the observed coincidences was evaluated through Monte Carlo…
We report the multi-wavelength identification of the X-ray sources found in the Subaru-XMM-Newton Deep Survey (SXDS) using deep imaging data covering the wavelength range between the far-UV to the mid-IR. We select a primary counterpart of…
Distinguishing active galaxies from star-forming galaxies is essential for understanding galaxy evolution. Diagnostic methods like the BPT (Baldwin, Phillips, and Terlevich) diagram use optical emission-line ratios to separate galaxies.…
Many open questions in X-ray astronomy are limited by the relatively small number of objects in uniform optically-identified samples, especially when rare subclasses are considered, or subsets isolated to search for evolution or…
The super-massive black-holes in the centers of Active Galactic Nuclei (AGNs) are surrounded by obscuring matter that can block the nuclear radiation. Depending on the amount of blocked radiation, the flux from the AGN can be too faint to…
X-ray extragalactic surveys are ideal laboratories for the study of the evolution and clustering of active galactic nuclei (AGN). The XXL Survey spans two fields of a combined 50 $deg^2$ observed for more than 6Ms with XMM-Newton, occupying…
To better understand the role of active galactic nuclei (AGN) in galaxy evolution, it is crucial to achieve a complete and pure AGN census. X-ray surveys are key to this, but identifying their counterparts (CTPs) at other wavelengths…
We report the detailed analysis of the X-ray properties of the discrete sources detected in a long (72 ks) \chandra ACIS-S observation of the Antennae galaxies. We detect 49 sources, down to a detection limit of ~ 10^{38} erg\s; 18 sources…
Inspired by the discovery of the Phoenix cluster by the South Pole Telescope team, we initiated a search for other massive clusters of galaxies missing from the standard X-ray catalogs. We began by identifying 25 cluster candidates not…
We present 348 X-ray emitting stars identified from correlating the Extended Chandra Multiwavelength Project (ChaMP), a serendipitous wide-area X-ray survey, with the Sloan Digital Sky Survey (SDSS). We use morphological star/galaxy…
Machine learning (ML) is increasingly used to support decision-making in the healthcare sector. While ML approaches provide promising results with regard to their classification performance, most share a central limitation, namely their…
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…
Multi-label classification is a type of classification task, it is used when there are two or more classes, and the data point we want to predict may belong to none of the classes or all of them at the same time. In the real world, many…
We have computed obscured AGN redshifts using the XZ method, adopting a broad treatment in which we employed a wide-ranging data set and worked primarily at the XZ counts sensitivity threshold, culminating with a redshift catalog containing…
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…