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Register a new user4FGLzoo. 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 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.
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.
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 at the time of trigger. We examine a total of 79 GRBs by comparing the observed counts over a range of time intervals to that expected from designated background orbits, as well as by using a joint likelihood technique to model the expected distribution of stacked counts. We find strong evidence for subthreshold emission at MeV to GeV energies using both techniques. This observed excess is detected during intervals that include and exceed the durations typically characterizing the prompt emission observed at keV energies and lasts at least 2700 s after the co-aligned burst trigger. By utilizing a novel cumulative likelihood analysis, we find that although a bursts prompt gamma-ray and afterglow X-ray flux both correlate with the strength of the subthreshold emission, the X-ray afterglow flux measured by Swifts X-ray Telescope (XRT) at 11 hr post trigger correlates far more significantly. Overall, the extended nature of the subthreshold emission and its connection to the bursts afterglow brightness lend further support to the external forward shock origin of the late-time emission detected by the LAT. These results suggest that the extended high-energy emission observed by the LAT may be a relatively common feature but remains undetected in a majority of bursts owing to instrumental threshold effects.
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 FRII and Steep Spectrum Radio Quasars (SSRQs), which show jets of radiation oriented away from the observers line of sight. MAGNs are very numerous and well studied in the lower energies of the electromagnetic spectrum but are not commonly observed in the gamma-ray energy range, because their inclination leads to the loss of relativistic boosting of the jet emission. The Large Area Telescope (LAT) on board the Fermi Gamma-ray Space Telescope in the 100 MeV -300 GeV energy range detected only 18 MAGNs (15 radio galaxies and 3 SSRQs) compared to 1144 blazars. Studying MAGNs and their environment in the gamma-ray sky is extremely interesting, because FRI and FRII radio galaxies are respectively considered the parent populations of BL Lacs and FSRQs, and these account for more than 50% of the known gamma-ray sources. The aim of this study is to hunt new gamma-ray MAGN candidates among the remaining blazars of uncertain type and unassociated AGNs, using machine learning techniques and other physical constraints when strict classifications are not available. We found 10 new MAGN candidates associated with gamma-ray sources. Their features are consistent with a source with a misaligned jet of radiation. This study reinforces the need for more systematic investigation of MAGNs in order to improve understanding of the radiation emission mechanisms and and the disparity of detection between more powerful and weaker gamma-ray AGNs.
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.
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