相关论文: Evaluating the optical classification of Fermi BCU…

Blazars are active galactic nuclei with relativistic jets pointed almost directly at Earth. Blazars are characterized by strong, apparently stochastic flux variability at virtually all observed wavelengths and timescales, from minutes to…

Flares are a well-studied aspect of the Sun's magnetic activity. Detecting and classifying solar flares can inform the analysis of contamination caused by stellar flares in exoplanet transmission spectra. In this paper, we present a…

In this study, we analyze the $\sim 16$ yr Fermi-LAT data of 160 blazars consisting of 92 flat-spectrum radio quasars (FSRQs), 64 BL Lacertae type objects (BL Lacs), and 4 blazar candidates of unknown type objects (BCU), and exhibit their…

We present a new method for identifying blazar candidates by examining the locus, i.e. the region occupied by the Fermi gamma-ray blazars in the three-dimensional color space defined by the WISE infrared colors. This method is a refinement…

This paper presents a comparison of six machine learning (ML) algorithms: GRU-SVM (Agarap, 2017), Linear Regression, Multilayer Perceptron (MLP), Nearest Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on the…

Fermi-LAT LCR provide continuous and regularly-sampled gamma-ray light curves, spanning about 14 years, for a large sample of blazars. The log-normal flux distribution and linear RMS-Flux relation of the light curves for a few of Fermi…

We discuss a new method for unveiling the possible blazar AGN nature among the numerous population of Unassociated Gamma-ray sources (UGS) in the Fermi catalogues. Our tool relies on positional correspondence of the Fermi object with X-ray…

A significant fraction ($\sim 30$ %) of the gamma-ray sources listed in the second $\textit{Fermi}$ LAT (2FGL) catalog is still of unknown origin, being not yet associated with counterparts at lower energies. Using the available information…

Machine learning algorithms have been used to determine probabilistic classifications of unassociated sources. Often classification into two large classes, such as Galactic and extra-galactic, is considered. However, there are many more…

AIMS. We are studying an unbiased sample of 42 nearby (z < 0.2) BL Lacertae objects with a multi-wavelength approach. The results of VLBI observations were presented in the first paper of this series. In this paper, we study the…

Based on the collected multiwavelength data, namely in the radio (NVSS, FIRST, RATAN-600), IR (WISE), optical (Pan-STARRS), UV (GALEX), and X-ray (ROSAT, Swift-XRT) ranges, we have performed a cluster analysis for the blazars of the…

Machine learning (ML) approaches have been used to develop highly accurate and efficient applications in many fields including bio-medical science. However, even with advanced ML techniques, cancer classification using gene expression data…

The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the…

Even after decades of multi-wavelength (MWL) observations, blazars still remain mysterious objects. Their extreme variability and variety of emission characteristics observed during different time periods make it hard to understand the…

During its first 2 years of mission the Fermi-LAT instrument discovered more than 1,800 gamma-ray sources in the 100 MeV to 100 GeV range. Despite the application of advanced techniques to identify and associate the Fermi-LAT sources with…

We present the results of a photon cluster search in the 7-years Fermi-Large Area Telescope extragalactic Pass 8 gamma-ray sky by means of the Minimum Spanning Tree (MST) algorithm, at energies higher than 10 GeV. We found 16 clusters of…

We present multi-epoch observations from the Submillimeter Array (SMA) for a sample of 171 bright blazars, 43 of which were detected by Fermi during the first three months of observations. We explore the correlation between their gamma-ray…

Long-term \textit{Fermi}-LAT monitoring makes it possible to ask whether a blazar light curve shows signs of an upcoming flare before the flare becomes obvious in the $\gamma$-ray emission. We present a strictly causal machine-learning…

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…

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…