Related papers: Explainable machine learning classification of \te…
A large number of unidentified sources found by astronomical surveys and other observations necessitate the use of an automated classification technique based on machine learning methods. The aim of this paper is to find a suitable…
The automatic classification of X-ray detections is a necessary step in extracting astrophysical information from compiled catalogs of astrophysical sources. Classification is useful for the study of individual objects, statistics for…
The rapid increase in serendipitous X-ray source detections requires the development of novel approaches to efficiently explore the nature of X-ray sources. If even a fraction of these sources could be reliably classified, it would enable…
A large fraction of Fermi-Large Area Telescope (LAT) sources in the fourth Fermi-LAT 14 yr catalog (4FGL) still remain unidentified (unIDed). We continued to improve our machine-learning pipeline and used it to classify 1206 X-ray sources…
In the latest data release from the Fermi $\gamma$-ray Space Telescope (the 4th Fermi LAT 14 yr Catalog, or 4FGL), more than 50% of the Galactic sources are yet to be identified. We observed 13 unidentified Fermi LAT sources with the…
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…
From optical spectroscopy of X-ray sources observed as part of ChaMP, we present redshifts and classifications for a total of 1569 Chandra sources from our targeted spectroscopic follow up using the FLWO, SAAO, WIYN, CTIO, KPNO, Magellan,…
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…
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…
Modern X-ray telescopes have detected hundreds of thousands of X-ray sources in the universe. However, current methods to classify these sources using the X-ray data themselves suffer problems - detailed X-ray spectroscopy of individual…
We employ the XGBoost machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0--S0a, Sa--Sb, Sbc--Scd, Sd--Irr) classes, using a combination of non-parametric…
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…
We have detected 523 sources in a survey of the Small Magellanic Cloud (SMC) Wing with Chandra. By cross-correlating the X-ray data with optical and near-infrared catalogues we have found 300 matches. Using a technique that combines X-ray…
Machine learning has emerged as a powerful tool in the field of gamma-ray astrophysics. The algorithms can distinguish between different source types, such as blazars and pulsars, and help uncover new insights into the high-energy universe.…
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…
Observations of present and future X-ray telescopes include a large number of serendipidious sources of unknown types. They are a rich source of knowledge about X-ray dominated astronomical objects, their distribution, and their evolution.…
We present followup observations of five hard X-ray sources from the ASCA 5-10 keV SHEEP survey, which has a limiting flux of $\sim 10^{-13}$ erg cm$^{-2}$ s$^{-1}$. Chandra data have been obtained to improve the X-ray positions from a few…
The Chandra Multi-wavelength Project (ChaMP) is a wide-area (~14 deg^2) survey of serendipitous Chandra X-ray sources, aiming to establish fair statistical samples covering a wide range of characteristics (such as absorbed AGNs, high z…
We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected…
The 13hr XMM-Newton/Chandra deep survey is the first of two extremely deep XMM-Newton fields observed by the XMM-OM consortium. A 120 ks Chandra mosaic, covering 0.2 square degrees, provides sensitive, confusion-free point source detection…