Research and characterisation of blazar candidates among the<i>Fermi</i>/LAT 3FGL catalogue using multivariate classifications

Lefaucheur, J.; Pita, S.

doi:10.1051/0004-6361/201629552

Cited by 42 publications

(39 citation statements)

References 30 publications

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“…We found that our results are mostly consistent with previous works presented in Chiaro et al (2016); Lefaucheur & Pita (2017); Yi et al (2017) and Kang et al (2019a) which utilize different statistical (e.g., SML) algorithms (see Table 4 and Table 3). The exceptions are as follows: 2 sources do not find matching sources and 2 sources did not provide a clear classification in Lefaucheur & Pita (2017). In addition, only 3 sources are classified as FSRQs in M clust Gaussian Mixture Modelling (M 8 ), and two are classified as FRSQs using support vector machine (SVM 8 ) using 8 parameters in Kang et al 2019a; 1 source is classified as an FSRQ in Chiaro et al 2016 (Chi16), whereas two sources are classified as FRSQs in Yi et al 2017 (Y17).…”

Section: Comparison With Literature Resultssupporting

confidence: 91%

An Empirical “High-confidence” Candidate Zone for Fermi BL Lacertae Objects

Kang

Zhu

Feng

et al. 2020

ApJ

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In the third catalog of active galactic nuclei detected by the F ermi Large Area Telescope Clean (3LAC) sample, there are 402 blazars candidates of uncertain type (BCU). The proposed analysis will help to evaluate the potential optical classification flat spectrum radio quasars (FSRQs) versus BL Lacertae (BL Lacs) objects of BCUs, which can help to understand which is the most elusive class of blazar hidden in the Fermi sample. By studying the 3LAC sample, we found some critical values of γ-ray photon spectral index (Γ ph ), variability index (VI) and radio flux (F R ) of the sources separate known FSRQs and BL Lac objects. We further utilize those values to defined an empirical "highconfidence" candidate zone that can be used to classify the BCUs. Within such a zone (Γ ph < 2.187, logF R < 2.258 and logVI < 1.702), we found that 120 BCUs can be classified BL Lac candidates with a higher degree of confidence (with a misjudged rate < 1%). Our results suggest that an empirical "high confidence" diagnosis is possible to distinguish the BL Lacs from the Fermi observations based on only on the direct observational data of Γ ph , VI and F R .

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Section: Comparison With Literature Resultssupporting

confidence: 91%

An Empirical “High-confidence” Candidate Zone for Fermi BL Lacertae Objects

Kang

Zhu

Feng

et al. 2020

ApJ

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“…Einecke (2016) search for high-confidence Blazar Candidates based 3FGL, an infrared and an X-ray catalog using a RF algorithm. In addition, Lefaucheur & Pita (2017) firstly identified blazar candidates from the 3FGL unassociated sources; they subsequently classified BL Lacs or FSRQs from these candidates and the BCUs reported in 3FGL using multivariate classifications. Furthermore, Salvetti et al (2017) used the 3FGL catalog and identified BL Lacs and FSRQs from 559 3FGL unassociated sources using an ANN algorithm.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Classification of Fermi BCUs from the 4FGL Catalog Using Machine Learning

Kang¹,

Li²,

Ou³

et al. 2019

ApJ

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The recently published fourth Fermi Large Area Telescope source catalog (4FGL) a)b) , 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 ≃ 91.8%, or ≃ 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.

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“…Note a source being in just one of them is discarded, i.e., we do not require an "and" criterium, but an "or". We reject a total of 162 3FGL sources with this method, which are not discarded by previous filters (the total number is 559 for [79] and 595 for [80]). Also, by cross-checking the results of these works for the 3FGL with the 2FHL and 3FHL catalogs, 7 3FHL sources are discarded.…”

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confidence: 99%

Unidentified gamma-ray sources as targets for indirect dark matter detection with theFermi-Large Area Telescope

Coronado-Blázquez

Domínguez

Aguirre-Santaella

et al. 2019

J. Cosmol. Astropart. Phys.

115

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One of the predictions of the ΛCDM cosmological framework is the hierarchical formation of structure, giving rise to dark matter (DM) halos and subhalos. When the latter are massive enough they retain gas (i.e., baryons) and become visible. This is the case of the dwarf satellite galaxies in the Milky Way (MW). Below a certain mass, halos may not accumulate significant amounts of baryons and remain completely dark. However, if DM particles are Weakly Interacting Massive Particles (WIMPs), we expect them to annihilate in subhalos, producing gamma rays which can be detected with the Fermi satellite. Using the three most recent point-source Fermi Large Area Telescope (LAT) catalogs (3FGL, 2FHL and 3FHL), we search for DM subhalo candidates among the unidentified sources, i.e., sources with no firm association to a known astrophysical object. We apply several selection criteria based on the expected properties of the DM-induced emission from subhalos, which allow us to significantly reduce the list of potential candidates. Then, by characterizing the minimum detection flux of the instrument and comparing our sample to predictions from the Via Lactea II (VL-II) N-body cosmological simulation, we place conservative and robust constraints on the σv − m DM parameter space. For annihilation via the τ + τ − channel, we put an upper limit of 4 × 10 −26 (5 × 10 −25 ) cm 3 s −1 for a mass of 10 (100) GeV. A critical improvement over previous treatments is the repopulation we made to include low-mass subhalos below the VL-II mass resolution. With more advanced subhalo candidate filtering the sensitivity reach of our method can potentially improve these constraints by a factor 3 (2) for τ + τ − (bb) channel.are an example of the most massive members of this population [11]. Yet, these dSphs are exceptional objects, in that they are massive enough to retain baryons (i.e., gas) and form stars. Conversely, the vast majority of the Galactic DM subhalos are not expected to host baryons and therefore remain completely dark [12]. Given their much larger number density, many of these small subhalos will be much closer to the Earth than the bigger ones, making them potentially interesting for dark matter searches.Should the Weakly Interacting Massive Particle (WIMP) DM model be correct (see, e.g., [13, 14] for a review), these objects may be detectable in the gamma-ray data. WIMPs can achieve the correct relic DM abundance (the so-called "WIMP miracle") through selfannihilation in the early Universe. Self-annihilation of WIMPs gives rise to a Standard Model (SM) particle-antiparticle pair which, among other possible subsequent by-products, typically yields gamma-ray photons. The ongoing self-annihilation of WIMPs in subhalos could be bright enough to be detectable.Since its launch in 2008, the Large Area Telescope on board the NASA Fermi Gammaray Space Telescope (Fermi-LAT) has been surveying the sky searching for gamma-ray sources [15]. The Fermi-LAT is a pair conversion telescope designed to observe the energy band from 20 MeV to gre...

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Research and characterisation of blazar candidates among theFermi/LAT 3FGL catalogue using multivariate classifications

Cited by 42 publications

References 30 publications

An Empirical “High-confidence” Candidate Zone for Fermi BL Lacertae Objects

An Empirical “High-confidence” Candidate Zone for Fermi BL Lacertae Objects

Evaluating the Classification of Fermi BCUs from the 4FGL Catalog Using Machine Learning

Unidentified gamma-ray sources as targets for indirect dark matter detection with theFermi-Large Area Telescope

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