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تسجيل مستخدم جديد4FGLzoo. Classifying Fermi-LAT uncertain gamma-ray sources by machine learning analysis
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Since 2008 August the Fermi Large Area Telescope (LAT) has provided continuous coverage of the gamma-ray sky yielding more than 5000 gamma-ray sources, but 54% of the detected sources remain with no certain or unknown association with a low energy counterpart. Rigorous determination of class type for a gamma-ray source requires the optical spectrum of the correct counterpart but optical observations are demanding and time-consuming, then machine learning techniques can be a powerful alternative for screening and ranking. We use machine learning techniques to select blazar candidates among uncertain sources characterized by gamma-ray properties very similar to those of Active Galactic Nuclei. Consequently, the percentage of sources of uncertain type drops from 54% to less than 12% predicting a new zoo for the Fermi gamma-ray sources. The result of this study opens up new considerations on the population of the gamma energy sky, and it will facilitate the planning of significant samples for rigorous analysis and multi-wavelength observational campaigns.
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Machine learning is an automatic technique that is revolutionizing scientific research, with innovative applications and wide use in astrophysics. The aim of this study was to developed an optimized version of an Artificial Neural Network machine lea
rning method for classifying blazar candidates of uncertain type detected by the Fermi Large Area Telescope (LAT) gamma-ray instrument. The initial study used information from gamma-ray light curves present in the LAT 4-year Source Catalog. In this study we used additionally gamma-ray spectra and multiwavelength data, and certain statistical methods in order to improve classification. The final result of this study increased the classification performance by about 80 per cent with respect to previous method, leaving only 15 unclassified blazars instead of 77 out of total 573 in the LAT catalog. Other blazars were classified into BL Lacs and FSRQ in ratio of about two to one, similar to previous study. In both studies a precision value of 90 per cent was used as a threshold for classification.
We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope (LAT) Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamm
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We perform a comprehensive stacking analysis of data collected by the Fermi Large Area Telescope (LAT) of gamma-ray bursts (GRB) localized by the Swift spacecraft, which were not detected by the LAT but which fell within the instruments field of view
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BL Lac Objects (BL Lacs) and Flat Spectrum Radio Quasars (FSRQs) are radio-loud active galaxies (AGNs) whose jets are seen at a small viewing angle (blazars), while Misaligned Active Galactic Nuclei (MAGNs) are mainly radiogalaxies of type FRI or FRI
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d Fermi-LAT observatories with the aim of investigating gamma-ray emission over a broad energy band. In this paper, we report the results from the first joint work between HAWC, MAGIC and Fermi-LAT on three unassociated HAWC sources: 2HWC J2006+341, 2HWC J1907+084* and 2HWC J1852+013*. Although no significant detection was found in the HE and VHE regimes, this investigation shows that a minimum 1 degree extension (at 95% confidence level) and harder spectrum in the GeV than the one extrapolated from HAWC results are required in the case of 2HWC J1852+013*, while a simply minimum extension of 0.16 degrees (at 95% confidence level) can already explain the scenario proposed by HAWC for the remaining sources. Moreover, the hypothesis that these sources are pulsar wind nebulae is also investigated in detail.
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