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Register a new userOptimizing neural network techniques in classifying Fermi-LAT gamma-ray sources
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Milo\\v{s} Kova\\v{c}evi\\'c
Publication date
2019
fields
Physics
and research's language is
English
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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 learning 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.
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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.
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 gamma-ray emitters: pulsars (PSR) or Active Galactic Nuclei (AGN). Using 1904 3FGL sources that have been identified/associated with AGN (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a sub-sample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (~90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of {it unassociated} sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g. binaries, SNR/PWN). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest for both in-depth follow-up searches (e.g. pulsar) at various wavelengths, as well as for broader population studies.
The HAWC Collaboration released the 2HWC catalog of TeV sources, in which 19 show no association with any known high-energy (HE; E > 10 GeV) or very-high-energy (VHE; E > 300 GeV) sources. This catalog motivated follow-up studies by both the MAGIC and 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.
We describe a long-term Swift monitoring program of Fermi gamma-ray sources, particularly the 23 gamma-ray sources of interest. We present a systematic analysis of the Swift X-ray Telescope light curves and hardness ratios of these sources, and we calculate excess variability. We present data for the time interval of 2004 December 22 through 2012 August 31. We describe the analysis methods used to produce these data products, and we discuss the availability of these data in an online repository, which continues to grow from more data on these sources and from a growing list of additional sources. This database should be of use to the broad astronomical community for long term studies of the variability of these objects and for inclusion in multi-wavelength studies.
Numerous extended sources around Galactic pulsars have shown significant $gamma$-ray emission from GeV to TeV energies, revealing hundreds of TeV energy electrons scattering off of the underlying photon fields through inverse Compton scattering (ICS). HAWC TeV gamma-ray observations of few-degree extended emission around the pulsars Geminga and Monogem, and LAT GeV emission around Geminga, suggest that systems older than 10-100 kyr have multi-TeV $e^pm$ propagating beyond the SNR-PWN system into the interstellar medium. Following the discovery of few $gamma$-ray sources by HAWC at energies E$>100$ TeV, we investigate the presence of an extended $gamma$-ray emission in Fermi-LAT data around the three brightest sources detected by HAWC up to 100 TeV. We find an extended emission of $theta_{68} = 1.00^{+0.05}_{-0.07}$ deg around eHWC J1825-134 and $theta_{68} = 0.71pm0.10$ deg eHWC J1907+063. The analysis with ICS templates on Fermi-LAT data point to diffusion coefficient values which are significantly lower than the average Galactic one. When studied along with HAWC data, the $gamma$-ray Fermi-LAT data provide invaluable insight into the very high-energy electron and positron parent populations.
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