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Register a new userSearching for AGN and Pulsar Candidates in 4FGL Unassociated Sources Using Machine Learning
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In the fourth emph{Fermi} Large Area Telescope source catalog (4FGL), 5064 $gamma$-ray sources are reported, including 3207 active galactic nuclei (AGNs), 239 pulsars, 1336 unassociated sources, 92 sources with weak association with blazar at low Galactic latitude and 190 other sources. We employ two different supervised machine learning classifiers, combined with the direct observation parameters given by the 4FGL fits table, to search for sources potentially classified as AGNs and pulsars in the 1336 unassociated sources. In order to reduce the error caused by the large difference in the sizes of samples, we divide the classification process into two separate steps in order to identify the AGNs and the pulsars. First, we select the identified AGNs from all of the samples, and then select the identified pulsars from the remaining. Using the 4FGL sources associated or identified as AGNs, pulsars, and other sources with the features selected through the K-S test and the random forest (RF) feature importance measurement, we trained, optimized, and tested our classifier models. Then, the models are applied to classify the 1336 unassociated sources. According to the calculation results of the two classifiers, we show the sensitivity, specificity, accuracy in each step, and the class of unassociated sources given by each classifier. The accuracy obtained in the first step is approximately $95%$; in the second step, the obtained overall accuracy is approximately $80%$. Combining the results of the two classifiers, we predict that there are 583 AGN-type candidates, 115 pulsar-type candidates, 154 other types of $gamma$-ray candidates, and 484 of uncertain types.
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Using the 100-m Effelsberg radio telescope operating at 1.36 GHz, we have performed a targeted radio pulsar survey of 289 unassociated gamma-ray sources discovered by the Large Area Telescope (LAT) aboard the Fermi satellite and published in the 1FGL catalogue (Abdo et al., 2010). This survey resulted in the discovery of millisecond pulsar J1745+1017, which resides in a short-period binary system with a low-mass companion, Mmin ~ 0.0137 Msun, indicative of `Black Widow type systems. A two-year timing campaign has produced a refined radio ephemeris, accurate enough to allow for phase-folding of the LAT photons, resulting in the detection of a dual-peaked gamma-ray light-curve, proving that PSR J1745+1017 is the source responsible for the gamma-ray emission seen in 1FGL J1745.5 + 1018 (2FGL J1745.6+1015; Nolan et al., 2012). We find the gamma-ray spectrum of PSR J1745+1017 to be well modelled by an exponentially-cut-off power law with cut-off energy 3.2 GeV and photon index 1.6. The observed sources are known to contain a further 10 newly discovered pulsars which were undetected in this survey. Our radio observations of these sources are discussed and in all cases limiting flux densities are calculated. The reasons behind the seemingly low yield of discoveries are also discussed.
The recently published fourth Fermi Large Area Telescope source catalog (4FGL) reports 5065 gamma-ray sources in terms of direct observational gamma-ray properties. Among the sources, the largest population is the Active Galactic Nuclei (AGN), which consists of 3137 blazars, 42 radio galaxies, and 28 other AGNs. The blazar sample comprises 694 flat-spectrum radio quasars (FSRQs), 1131 BL Lac-type objects (BL Lacs), and 1312 blazar candidates of an unknown type (BCUs). The classification of blazars is difficult using optical spectroscopy given the limited knowledge with respect to their intrinsic properties, and the limited availability of astronomical observations. To overcome these challenges, machine learning algorithms are being investigated as alternative approaches. Using the 4FGL catalog, a sample of 3137 Fermi blazars with 23 parameters is systematically selected. Three established supervised machine learning algorithms (random forests (RFs), support vector machines (SVMs), artificial neural networks (ANNs)) are employed to general predictive models to classify the BCUs. We analyze the results for all of the different combinations of parameters. Interestingly, a previously reported trend the use of more parameters leading to higher accuracy is not found. Considering the least number of parameters used, combinations of eight, 12 or 10 parameters in the SVM, ANN, or RF generated models achieve the highest accuracy (Accuracy $simeq$ 91.8%, or $simeq$ 92.9%). Using the combined classification results from the optimal combinations of parameters, 724 BL Lac type candidates and 332 FSRQ type candidates are predicted; however, 256 remain without a clear prediction.
We report the results of searching pulsar-like candidates from the unidentified objects in the $3^{rm rd}$ Catalog of Hard Fermi-LAT sources (3FHL). Using a machine-learning based classification scheme with a nominal accuracy of $sim98%$, we have selected 27 pulsar-like objects from 200 unidentified 3FHL sources for an identification campaign. Using archival data, X-ray sources are found within the $gamma-$ray error ellipses of 10 3FHL pulsar-like candidates. Within the error circles of the much better constrained X-ray positions, we have also searched for the optical/infrared counterparts and examined their spectral energy distributions. Among our short-listed candidates, the most secure identification is the association of 3FHL J1823.3-1339 and its X-ray counterpart with the globular cluster Mercer 5. The $gamma-$rays from the source can be contributed by a population of millisecond pulsars residing in the cluster. This makes Mercer 5 as one of the slowly growing hard $gamma-$ray population of globular clusters with emission $>10$ GeV. Very recently, another candidate picked by our classification scheme, 3FHL J1405.1-6118, has been identified as a new $gamma-$ray binary with an orbital period of $13.7$ days. Our X-ray analysis with a short Chandra observation has found a possible periodic signal candidate of $sim1.4$ hrs and a putative extended X-ray tail of $sim20$ arcsec long. Spectral energy distribution of its optical/infrared counterpart conforms with a blackbody of $T_{rm bb}sim40000$ K and $R_{rm bb}sim12R_{odot}$ at a distance of 7.7 kpc. This is consistent with its identification as an early O star as found by infrared spectroscopy.
The detection of dark matter subhalos without a stellar component in the Galactic halo remains a challenge. We use supervised machine learning to identify high-latitude gamma-ray sources with dark matter-like spectra among unassociated gamma-ray sources in the 4FGL-DR2. Out of 843 4FGL-DR2 unassociated sources at $|b| geq 10mathrm{^circ}$, we select 73 dark matter subhalo candidates. Of the 69 covered by the Neil Gehrels Swift Observatory (Swift), 17 show at least one X-ray source within the 95% LAT error ellipse and 52 where we identify no new sources. This latest inventory of dark subhalos candidates allows us to investigate the possible dark matter substructure responsible for the perturbation in the GD-1 stellar stream. In particular, we examine the possibility that the alleged GD-1 dark subhalo may appear as a 4FGL-DR2 gamma-ray source from dark matter annihilation into Standard Model particles.
We present a qualitative search for ultra-fast outflows (UFOs) in excess variance spectra of radio-quiet active galactic nuclei (AGN). We analyse 42 sources from the Tombesi et al. (2010) spectroscopic UFO detection sample, and an additional 22 different sources from the Kara et al. (2016) variability sample. A total of 58 sources have sufficient observational data from XMM-Newton EPIC-pn and variability for an excess variance spectrum to be calculated. We examine these spectra for peaks corresponding to variable blue-shifted H- and He-like ion absorption lines from UFOs. We find good evidence for such outflows in 28% of the AGN sample and weak evidence in a further 31%, meaning that $sim$ 30-60% of the AGN sample hosts such UFOs. The mean and median blue-shifted velocity is found to be $sim$ 0.14c and 0.12c, respectively. Current variability methods allow for a fast, model-independent determination of UFOs, however, further work needs to be undertaken to better characterize the statistical significance of the peaks in these spectra by more rigorous modelling. Detecting good evidence for variable UFO lines in a large number of sources also lays the groundwork for detailed analysis of the variability timescales of the absorbers. This will allow us to probe their densities and hence distances from the central super-massive black hole.
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