Related papers: Machine learning methods for constructing probabil…

Large fraction of studies of active galactic nuclei objects is based on performing follow-up observations using high-sensitivity instruments of high flux states observed by monitoring instruments (the so-called Target of Opportunity, ToO).…

Machine learning, algorithms to extract empirical knowledge from data, can be used to classify data, which is one of the most common tasks in observational astronomy. In this paper, we focus on Bayesian data classification algorithms using…

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…

We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that…

Nearly 50% of all sources detected by the Fermi Large Area Telescope are classified as blazars or blazar candidates, one of the most elusive classes of active galaxies. Additional blazars can also be hidden within the sample of unidentified…

Radio synchrotron emission originates from both massive star formation and black hole accretion, two processes that drive galaxy evolution. Efficient classification of sources dominated by either process is therefore essential for fully…

Machine-learning (ML) algorithms will play a crucial role in studying the large datasets delivered by new facilities over the next decade and beyond. Here, we investigate the capabilities and limits of such methods in finding galaxies with…

A large fraction of the anticipated source detections by the Gamma-ray Large Area Space Telescope (GLAST-LAT) will initially be unidentified. We argue that traditional approaches to identify individuals and/or populations of gamma ray…

Machine learning (ML) algorithms become increasingly important in the analysis of astronomical data. However, since most ML algorithms are not designed to take data uncertainties into account, ML based studies are mostly restricted to data…

We report the detection of a cross-correlation signal between {\it Fermi} Large Area Telescope diffuse gamma-ray maps and catalogs of clusters. In our analysis, we considered three different catalogs: WHL12, redMaPPer and PlanckSZ. They all…

The global contribution of unresolved gamma-ray point sources to the extragalactic gamma-ray background has been recently measured down to gamma-ray fluxes lower than those reached with standard source detection techniques, and by employing…

Machine learning techniques have been increasingly useful in astronomical applications over the last few years, for example in the morphological classification of galaxies. Convolutional neural networks have proven to be highly effective in…

We describe a catalogue of gamma-ray source candidates, selected using the Minimum Spanning Tree (MST) algorithm on the 9-years Fermi-LAT sky (Pass 8) at energies higher than 10 GeV. The extragalactic sky at absolute Galactic latitudes…

Classification of galaxy morphology is a challenging but meaningful task for the enormous amount of data produced by the next-generation telescope. By introducing the adaptive polar coordinate transformation, we develop a rotationally…

Analysis is presented on 15 months of data taken with the Large Area Telescope (LAT) on the Fermi Gamma-ray Space Telescope for 11 non-blazar AGNs, including 7 FRI radio galaxies and 4 FRII radio sources consisting of 2 FRII radio galaxies…

Morphological classification is a key piece of information to define samples of galaxies aiming to study the large-scale structure of the universe. In essence, the challenge is to build up a robust methodology to perform a reliable…

One of the largest uncertainties in the Point Source (PS) studies, at Fermi-LAT energies, is the uncertainty in the diffuse background. In general there are two approaches for PS analysis: background-dependent methods, that include modeling…

An equivalent width (EW) based classification may cause the erroneous judgement to the flat spectrum radio quasars (FSRQs) and BL Lacerate objects (BL Lac) due to the diluting the line features by dramatic variations in the jet continuum…

We present an incremental version (4FGL-DR4, for Data Release 4) of the fourth Fermi-LAT catalog containing 7194 gamma-ray sources. Based on the first 14 years of science data in the energy range from 50 MeV to 1 TeV, it uses the same…

We examine three approaches to the problem of source classification in catalogues. Our goal is to determine the confidence with which the elements in these catalogues can be distinguished in populations on the basis of their spectral energy…