Multi-wavelength characterization of Fermi blazars of uncertain type

Paliya, Vaidehi S.

doi:10.1007/s12036-022-09803-0

Cited by 1 publication

(5 citation statements)

References 13 publications

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“…3. We use our result to make an overlap with Paliya (2022) and Agarwal (2023) and achieve a high degree of consistency. 4.…”

Section: Discussionmentioning

confidence: 99%

“…Interestingly, those BCUs from Paliya (2022) have optical spectra revealing strong/faint broad emission lines. Traditional classification based on equivalent width has certain limitations (Ghisellini et al 2011;Paliya 2022). Paliya et al (2021) proposed another scheme based on accretion rate and Compton dominance.…”

Section: Performance Of Modelsmentioning

confidence: 99%

“…Paliya et al (2021) proposed another scheme based on accretion rate and Compton dominance. Sources with a accretion rate ≈1% and Compton dominance ≈1 possibly belong to masquerading BL Lac objects (Giommi et al 1914;Paliya 2022). However, our work provides a prior method to classify Fermi BCUs for a statistical work.…”

Section: Performance Of Modelsmentioning

confidence: 99%

“…In 4FGL-DR3, it still remains about 40%. This mainly arises from two reasons: due to a lack of optical spectroscopy or due to an extremely poor signal-to-noise ratio (S/N) of the optical spectrum (Ackermann et al 2011;Paliya 2022). The classification of BCUs can provide more information about the extragalactic high-energy γ-ray sky.…”

Section: Introductionmentioning

confidence: 99%

“…The "CLASS_TW" is our predicted result. The "CLASS_P" is the result ofPaliya (2022). The "CLASS_A" is the result ofAgarwal (2023).…”

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confidence: 99%

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Classification of Fermi BCUs Using Machine Learning

Xiao,

Xie,

Zeng

et al. 2023

ApJ

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The Fermi Large Area Telescope (LAT) has detected 6659 γ-ray sources in the incremental version (4FGL-DR3, for Data Release 3) of the fourth Fermi-LAT catalog of γ-ray sources and 3743 of them are blazars, including 1517 blazar candidates of uncertain type (BCUs). Blazars are generally classified by properties of emission lines into BL Lac objects and flat spectrum radio quasars (FSRQs). However, BCUs are difficult to classify because of the lack of spectrum. In this work we apply five different machine-learning algorithms (K-nearest neighbors, logistic regression, support vector machine, random forest, CatBoost) to evaluate the classification of 1517 BCUs based on the observational data of 4FGL-DR3. The results indicate that the use of recursive feature elimination cross-validation can effectively improve the accuracy of models and reduce computation time. We use our models to predict the BCUs from 4FGL-DR3 and the results of the overlapping of the five models are as follows: 811 BL Lac objects, 397 FSRQs, and 309 BCUs.

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“…3. We use our result to make an overlap with Paliya (2022) and Agarwal (2023) and achieve a high degree of consistency. 4.…”

Section: Discussionmentioning

confidence: 99%