اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدMachine learning methods for constructing probabilistic Fermi-LAT catalogs
64
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Classification of sources is one of the most important tasks in astronomy. Sources detected in one wavelength band, for example using gamma rays, may have several possible associations in other wavebands or there may be no plausible association candidates. In this work, we aim to determine probabilistic classification of unassociated sources in the third and the fourth data release 2 Fermi Large Area Telescope (LAT) point source catalogs (3FGL and 4FGL-DR2) into two classes (pulsars and active galactic nuclei (AGNs)) or three classes (pulsars, AGNs, and other sources). We use several machine learning (ML) methods to determine probabilistic classification of Fermi-LAT sources. We evaluate the dependence of results on meta-parameters of the ML methods, such as the maximal depth of the trees in tree-based classification methods and the number of neurons in neural networks. We determine probabilistic classification of both associated and unassociated sources in 3FGL and 4FGL-DR2 catalogs. We cross-check the accuracy by comparing the predicted classes of unassociated sources in 3FGL that have associations in 4FGL-DR2. We find that in the 2-class case it is important to correct for the presence of other sources among the unassociated ones in order to realistically estimate the number of pulsars and AGNs. In particular, the estimated number of pulsars in the 3FGL (4FGL-DR2) catalog is 270 (483) in the 2-class case without corrections for the other sources and 158 (215) in the 3-class case. Provided that the number of associated pulsars is 167 (271) in the 3FGL (4FGL-DR2) catalog, the number of pulsars among the unassociated sources is expected to be similar or larger than the number of associated ones.
قيم البحث
اقرأ أيضاً
A novel application of machine-learning (ML) based image processing algorithms is proposed to analyze an all-sky map (ASM) obtained using the Fermi Gamma-ray Space Telescope. An attempt was made to simulate a one-year ASM from a short-exposure ASM ge
nerated from one-week observation by applying three ML based image processing algorithms: dictionary learning, U-net, and Noise2Noise. Although the inference based on ML is less clear compared to standard likelihood analysis, the quality of the ASM was generally improved. In particular, the complicated diffuse emission associated with the galactic plane was successfully reproduced only from one-week observation data to mimic a ground truth (GT) generated from a one-year observation. Such ML algorithms can be implemented relatively easily to provide sharper images without various assumptions of emission models. In contrast, large deviations between simulated ML maps and GT map were found, which are attributed to the significant temporal variability of blazar-type active galactic nuclei (AGNs) over a year. Thus, the proposed ML methods are viable not only to improve the image quality of an ASM, but also to detect variable sources, such as AGNs, algorithmically, i.e., without human bias. Moreover, we argue that this approach is widely applicable to ASMs obtained by various other missions; thus, it has the potential to examine giant structures and transient events, both of which are rarely found in pointing observations.
We report the results of a cross-match study between the hard X-ray and GeV gamma-ray catalogs, by making use of the latest 105-month Swift-BAT and 10-yr Fermi-LAT catalogs, respectively. The spatial cross-matching between the two catalogs results in
the matching of 132 point-like sources, including ~5% of false-match sources. Additionally, 24 sources that have been identified as the same identifications are matched. Among the 75 extended sources in the Fermi-LAT catalog, 31 sources have spatial coincidences with at least one Swift-BAT source inside their extent. All the matched sources consist of blazars (>60%), pulsars and pulsar wind nebulae (~13%), radio galaxies (~7%), binaries (~5%), and others. Compared to the original catalogs, the matched sources are characterized by a double-peaked photon index distribution, higher flux, and larger gamma-ray variability index. This difference arises from the different populations of sources, particularly the large proportion of blazars (i.e., FSRQ and BL Lac). We also report 13 cross-matched and unidentified sources. The matched sources in this study would be promising in the intermediate energy band between the hard X-ray and GeV gamma-ray observations, that is the unexplored MeV gamma-ray domain.
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 co
unterpart. 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 present results of a study of the localization capability of Fermi-LAT, using a large set of blazars with precise radio locations. Since the width of the PSF decreases with energy, the performance is typically dominated by a few high energy photon
s, so it is important to properly characterize the high-energy PSF. Using such data, we have found a need to modify the pre-launch high-energy (greater than a few GeV) PSF derived from extensive Monte Carlo simulations of particle interactions in the LAT; the resulting data-based PSF is shown
To uniformly determine the properties of supernova remnants (SNRs) at high energies, we have developed the first systematic survey at energies from 1 to 100 GeV using data from the Fermi Large Area Telescope. Based on the spatial overlap of sources d
etected at GeV energies with SNRs known from radio surveys, we classify 30 sources as likely GeV SNRs. We also report 14 marginal associations and 245 flux upper limits. A mock catalog in which the positions of known remnants are scrambled in Galactic longitude, allows us to determine an upper limit of 22% on the number of GeV candidates falsely identified as SNRs. We have also developed a method to estimate spectral and spatial systematic errors arising from the diffuse interstellar emission model, a key component of all Galactic Fermi LAT analyses. By studying remnants uniformly in aggregate, we measure the GeV properties common to these objects and provide a crucial context for the detailed modeling of individual SNRs. Combining our GeV results with multiwavelength (MW) data, including radio, X-ray, and TeV, demonstrates the need for improvements to previously sufficient, simple models describing the GeV and radio emission from these objects. We model the GeV and MW emission from SNRs in aggregate to constrain their maximal contribution to observed Galactic cosmic rays.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
