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

Kang, S. J.; Zhu, Kongfu; Feng, Jian‐Chao; Wu, Qingwen; Zhang, Bin-Bin; Yin, Yue; Wang, Feifei; Liu, Yu; Zheng, Tian-Yuan

doi:10.3847/1538-4357/ab722d

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…The K-S test is a two-sample hypothesis test method, which is often used to evaluate the significance of the distribution difference of the same measurement in two samples (e.g., Xiong & Zhang 2014;Kang et al 2020). In particular, the K-S test can also be applied for feature selection (e.g., Kang et al 2019a,b), based on the principle that the greater the distribution difference of the two samples over a feature, the more favorable the feature is in SML classifiers.…”

Section: Dataset Preparationmentioning

confidence: 99%

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

Zhu

Kang

Zheng

2021

Res. Astron. Astrophys.

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In the fourth Fermi Large Area Telescope source catalog (4FGL), 5064 γ-ray sources are reported, including 3207 active galactic nuclei (AGNs), 239 pulsars, 1336 unassociated sources, 92 sources with weak association with blazars at low Galactic latitudes 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 cases. 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 report 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 γ-ray candidates and 484 of uncertain types.

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Section: Dataset Preparationmentioning

confidence: 99%

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

Zhu

Kang

Zheng

2021

Res. Astron. Astrophys.

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“…These sources were subsequently designated as the LAT Bright AGN Sample (Abdo et al 2009(Abdo et al , 2010. For specific luminous FSRQ populations characterized by relatively high-energy γ-ray emissions, investigating their broadband characteristics to establish reasonable physical constraints remains a valuable avenue for research (Abdo et al 2009(Abdo et al , 2010Sikora et al 2009;Zhang et al 2012;Kang et al 2014Kang et al , 2020Paliya et al 2018;Anjum et al 2020;Roy et al 2021;Zhou et al 2021;Zhu et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Modeling the Multiwavelength Spectral Energy Distributions of the Fermi-4LAC Bright Flat-spectrum Radio Quasars

Zhou,

Zheng,

Zhu

et al. 2024

ApJ

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In this paper, we present a long-term multiwavelength investigation focusing on 12 distinct samples of Fermi-4LAC bright flat-spectrum radio quasars (FSRQs). Detailed variability and spectral analyses of γ-ray, X-ray, and ultraviolet/optical data obtained by the Fermi Large Area Telescope, the Swift X-ray Telescope, and the Swift Ultraviolet and Optical Telescope were performed over a period of about 14 yr, spanning from 2008 October to 2022 October. These analyses provide insights into characterizing the variations within different activity states. To efficiently reproduce the multiwavelength simultaneous/quasi-simultaneous spectral energy distributions (SEDs) of the samples, we propose a novel approach for constraining the model parameters. By analyzing the parameters of the energy spectral curvature (β), the peak frequency (ν pk), the peak luminosity (L pk), the Compton dominance parameter (A C), and the variability timescale (t var) in different activity states, we can estimate the values of the jet radiation region parameters for the samples. Subsequently, we utilize the synchrotron-self-Compton and external Compton processes, employing a logarithmic-parabolic spectral shape to approximate the observed spectra of the sample sources, while considering the induced regime for the physical parameters. The model results show that: (1) by effectively reproducing SEDs in various active states of bright FSRQs, the parameters within the emission region were reasonably constrained; (2) compared to other active states, the emission region of the jet exhibits a reduced radius during the high state, while the magnetic field strength increases during the low state; and (3) for bright FSRQs in a high-activity state, there is an enhancement of the Doppler factor, often exhibiting a tendency toward energy equipartition.

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Hunting for the candidates of misclassified sources in LSP BL Lacs using machine learning

Kang,

Zheng,

2023

Monthly Notices of the Royal Astronomical Society

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An equivalent width (EW) based classification may cause the erroneous judgement to the flat spectrum radio quasars (FSRQs) and BL Lacerate objects (BL Lac) due to the diluting the line features by dramatic variations in the jet continuum flux. To help address the issue, the present paper explore the possible intrinsic classification on the bias of a random forest supervised machine learning algorithm. In order to do so, we compile a sample of 1680 Fermi blazars that have both gamma-rays and radio-frequencies data available from the 4LAC-DR2 catalog, which includes 1352 training and validation samples and 328 forecast samples. By studying the results for all of the different combinations of 23 characteristic parameters, we found that there are 178 optimal parameters combinations (OPCs) with the highest accuracy (≃ 98.89%). Using the combined classification results from the nine combinations of these OPCs to the 328 forecast samples, we predict that there are 113 true BL Lacs (TBLs) and 157 false BL Lacs (FBLs) that are possible intrinsically FSRQs misclassified as BL Lacs. The FBLs show a clear separation from TBLs and FSRQs in the γ-ray photon spectral index, Γph, and X-band radio flux, logFR, plot. Phenomenally, existence a BL Lac to FSRQ (B-to-F) transition zone is suggested, where the FBLs are in the stage of transition from BL Lacs to FSRQs. Comparing the LSP Changing-Look Blazars (CLBs) reported in the literatures, the majority of LSP CLBs are located at the B-to-F zone. We argue that the FBLs located at B-to-F transition zone are the most likely Candidates of CLBs.

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

Cited by 3 publications

References 47 publications

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

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

Modeling the Multiwavelength Spectral Energy Distributions of the Fermi-4LAC Bright Flat-spectrum Radio Quasars

Hunting for the candidates of misclassified sources in LSP BL Lacs using machine learning

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