Searching for AGN and pulsar candidates in 4FGL unassociated sources using machine learning

Zhu, Kongfu; Kang, S. J.; Zheng, Y. G.

doi:10.1088/1674-4527/21/1/15

Cited by 14 publications

(13 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Classification of Fermi -LAT 4FGL sources into three classes has been considered earlier by, for example, Zhu et al (2021), who primarily use a two-step classification procedure, where in the first step AGNs are separated from the rest of sources and in the second step the remaining sources are split into pulsars and OTHER sources. Zhu et al (2021) have also tested a simultaneous classification of sources into three classes (AGN, pulsars, and OTHER), but the results are inconsistent for the two ML algorithms used by Zhu et al (2021) (RF and NN). In particular, the number of OTHER sources predicted by NN is zero.…”

Section: Notesmentioning

confidence: 99%

“…Bayesian association probabilities were also included in the 4FGL catalog (Abdollahi et al 2020) for faint sources. Probabilistic classification of unassociated Fermi -LAT sources was performed by, for example, Ackermann et al (2012), Saz Parkinson et al (2016, Mirabal et al (2016), Lefaucheur & Pita (2017), Luo et al (2020), Finke et al (2021), and Zhu et al (2021), or in the application for the subclassification of blazars by Hassan et al (2013), Doert & Errando (2014), Chiaro et al (2016), Salvetti et al (2017), and Kovačević et al (2019Kovačević et al ( , 2020, and in subclassification of pulsars by Lee et al (2012) and Saz Parkinson et al (2016). In this work we considered the classification of gamma-ray sources into two classes -active galactic nuclei (AGNs) and pulsars -as well as into three classes -AGNs, pulsars, and other associated sources ("OTHER").…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning methods for constructing probabilistic Fermi-LAT catalogs

Bhat¹,

Malyshev²

2022

A&A

View full text Add to dashboard Cite

Context. 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. Aims. In this work we aim to determine the probabilistic classification of unassociated sources in the third Fermi Large Area Telescope (LAT) point source catalog (3FGL) and the fourth Fermi LAT data release 2 point source catalog (4FGL-DR2) using two classes – pulsars and active galactic nuclei (AGNs) – or three classes – pulsars, AGNs, and “OTHER” sources. Methods. We use several machine learning (ML) methods to determine a probabilistic classification of Fermi-LAT sources. We evaluate the dependence of results on the 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. Results. We determine a probabilistic classification of both associated and unassociated sources in the 3FGL and 4FGL-DR2 catalogs. We cross-check the accuracy by comparing the predicted classes of unassociated sources in 3FGL with their associations in 4FGL-DR2 for cases where such associations exist. We find that in the two-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. We find that the three-class classification, despite different types of sources in the OTHER class, has a similar performance as the two-class classification in terms of reliability diagrams and, at the same time, it does not require adjustment due to presence of the OTHER sources among the unassociated sources. We show an example of the use of the probabilistic catalogs for population studies, which include associated and unassociated sources.

show abstract

Section: Notesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning methods for constructing probabilistic Fermi-LAT catalogs

Bhat¹,

Malyshev²

2022

A&A

View full text Add to dashboard Cite

show abstract

“…2. PTC SELECTION USING ML ML techniques are popular in the field of astronomical data mining and data analysis (Ball & Brunner 2010;Mirabal et al 2012;Chiaro et al 2016;Saz Parkinson et al 2016;Salvetti et al 2017;Baron 2019;Kang et al 2019a,b;Arsioli & Dedin 2020;Fraga et al 2021;Zhu et al 2021). According to whether the classification of a sample is given, ML can be mainly divided into supervised machine-learning (SML) and unsupervised machine-learning (USML) techniques (Baron 2019).…”

Section: Introductionmentioning

confidence: 99%

“…When evaluating a classifier, there are different metrics for performance evaluation that can be used, such as accuracy, recall, precision, and the receiver operating characteristic (ROC) curve (Baron 2019). Accuracy is widely used, but simple classification accuracy is usually dominated by large samples from imbalanced data sets (Zhu et al 2021). In this study, we used the balanced-accuracy (i.e., the average accuracy of positive and negative samples) rather than simple accuracy to evaluate the classifier performance.…”

Section: Introductionmentioning

confidence: 99%

Searching for TeV Candidates in 4LAC High-synchrotron-peaked Frequency BL Lac Objects

Zhu,

Kang,

Zhou

et al. 2021

Preprint

View full text Add to dashboard Cite

The next generation of TeV detectors is expected to have a significantly enhanced performance. It is therefore constructive to search for new TeV candidates for observation. This paper focuses on TeV candidates among the high-synchrotron-peaked BL Lacertae objects (HBLs) reported in the fourth catalog of active galactic nuclei detected by the Fermi's Large Area Telescope, i.e., 4LAC. By cross-matching the Fermi data with radio and optical observations, we collected the multiwavelength features of 180 HBLs with known redshift. The data set contains 39 confirmed TeV sources and 141 objects whose TeV detection has not yet been reported (either not yet observed, or observed but not detected). Using two kinds of supervised machine-learning (SML) methods, we searched for new possible TeV candidates (PTCs) among the nondetected objects by assessing the similarity of their multi-wavelength properties to existing TeV-detected objects. The classification results of the two SML classifiers were combined and the 24 highest-confidence PTCs were proposed as the best candidates. We calculate, here, the 12 year averaged Fermi spectra of these PTCs and estimate their detectability by extrapolating the Fermi spectrum and including the extragalactic background light attenuation. Four candidates are suggested to have a high likelihood of being detected by the Large High Altitude Air Shower Observatory and 24 are candidates for the Cerenkov Telescope Array observations.

show abstract

“…Searching in the radio band is similarly difficult, requiring assumptions to link gamma-ray and radio emission without interpreting the intermediate wavelengths, but the detection of characteristic radio pulsations is a direct route to locating pulsars and some previous works have used radio properties to predict pulsar membership after efficient searches (for example, Frail et al 2018). Recently, Zhu et al (2021) conducted ML on the 4FGL unassociated sources, but restricted their analysis to only gamma-ray properties. Analysis of X-ray observations specifically can capture the synchrotron peaks of non-thermal emitters like blazars, a valuable region for discriminating the spectra of pulsars and blazars.…”

mentioning

confidence: 99%

Multiwavelength Spectral Analysis and Neural Network Classification of Counterparts to 4FGL Unassociated Sources

Kerby,

Kaur,

Falcone

et al. 2021

Preprint

View full text Add to dashboard Cite

The Fermi -LAT unassociated sources represent some of the most enigmatic gamma-ray sources in the sky. Observations with the Swift-XRT and -UVOT telescopes have identified hundreds of likely X-ray and UV/optical counterparts in the uncertainty ellipses of the unassociated sources. In this work we present spectral fitting results for 205 possible X-ray/UV/optical counterparts to 4FGL unassociated targets. Assuming that the unassociated sources contain mostly pulsars and blazars, we develop a neural network classifier approach that applies gamma-ray, X-ray, and UV/optical spectral parameters to yield descriptive classification of unassociated spectra into pulsars and blazars. From our primary sample of 174 Fermi sources with a single X-ray/UV/optical counterpart, we present 132 P bzr > 0.99 likely blazars and 14 P bzr < 0.01 likely pulsars, with 28 remaining ambiguous. These subsets of the unassociated sources suggest a systematic expansion to catalogs of gamma-ray pulsars and blazars. Compared to previous classification approaches our neural network classifier achieves significantly higher validation accuracy and returns more bifurcated P bzr values, suggesting that multiwavelength analysis is a valuable tool for confident classification of Fermi unassociated sources.

show abstract

Searching for AGN and pulsar candidates in 4FGL unassociated sources using machine learning

Cited by 14 publications

References 63 publications

Machine learning methods for constructing probabilistic Fermi-LAT catalogs

Machine learning methods for constructing probabilistic Fermi-LAT catalogs

Searching for TeV Candidates in 4LAC High-synchrotron-peaked Frequency BL Lac Objects

Multiwavelength Spectral Analysis and Neural Network Classification of Counterparts to 4FGL Unassociated Sources

Contact Info

Product

Resources

About