Classifying Blazar Candidates from the 3FGL Unassociated Catalog into BL Lacertae Objects and Flat Spectrum Radio Quasars Using Swift and WISE Data

Kaur, A.; Falcone, A.; Stroh, M. C.

doi:10.3847/1538-4357/abd324

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…Using a combination of RF and ANN, Zhu et al (2021) studied 1336 unassociated sources and classified them as 583 AGN-like sources, 115 pulsars, 154 other classes, and 484 of uncertain type. Kaur et al (2021) utilized RF to classify Fermi 3FGL unassociated γ-ray sources as BL Lac objects and FSRQs. A sample of 84 highly likely blazar candidates was then classified as 50 likely BL Lac objects, and 34 were ambiguous.…”

Section: Introductionmentioning

confidence: 99%

Classification of Blazar Candidates of Unknown Type in Fermi 4LAC by Unanimous Voting from Multiple Machine-learning Algorithms

Agarwal

2023

ApJ

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The Fermi fourth catalog of active galactic nuclei (AGNs) data release 3 (4LAC-DR3) contains 3407 AGNs, out of which 755 are flat spectrum radio quasars (FSRQs), 1379 are BL Lacertae objects (BL Lac objects), 1208 are blazars of unknown (BCUs) type, while 65 are non-AGNs. Accurate categorization of many unassociated blazars still remains a challenge due to the lack of sufficient optical spectral information. The aim of this work is to use high-precision, optimized machine-learning (ML) algorithms to classify BCUs into BL Lac objects and FSRQs. To address this, we selected the 4LAC-DR3 Clean sample (i.e., sources with no analysis flags) containing 1115 BCUs. We employ five different supervised ML algorithms, namely, random forest, logistic regression, XGBoost, CatBoost, and neural network with seven features: photon index, synchrotron-peak frequency, pivot energy, photon index at pivot energy, fractional variability, ν F ν, at synchrotron-peak frequency, and variability index. Combining results from all models leads to better accuracy and more robust predictions. These five methods together classified 610 BCUs as BL Lac objects and 333 BCUs as FSRQs with a classification metric area under the curve >0.96. Our results are significantly compatible with recent studies as well. The output from this study provides a larger blazar sample with many new targets that could be used for forthcoming multiwavelength surveys. This work can be further extended by adding features in X-rays, UV, visible, and radio wavelengths.

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Section: Introductionmentioning

confidence: 99%

Classification of Blazar Candidates of Unknown Type in Fermi 4LAC by Unanimous Voting from Multiple Machine-learning Algorithms

Agarwal

2023

ApJ

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“…The study found that FSRQs exhibit a similar SED across a wide range of L γ , while BL Lacs display decreasing synchrotron peak frequencies with increasing L γ . Furthermore, the nature of blazars at various wavelengths has been discussed by several studies (Richards et al 2011;Landi et al 2015;Acciari et al 2021;Kaur et al 2021;Kerby et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Optical Variability of Blazars in the Tomo-e Gozen Northern Sky Transient Survey

Zhang,

Doi,

Kokubo

et al. 2024

ApJ

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We studied the optical variability of 241 BL Lacertae (BL Lacs) and 83 flat-spectrum radio quasars (FSRQs) from the 4LAC catalog using data from the Tomo-e Gozen Northern Sky Transient Survey, with ∼50 epochs per blazar on average. We excluded blazars whose optical variability may be underestimated due to the influence of their host galaxy based on their optical luminosity (L O ). FSRQs with γ-ray photon index greater than 2.6 exhibit very low optical variability, and their distribution of standard deviation of repeated photometry is significantly different from that of the other FSRQs (Kolmogorov–Smirnov test p-value equal to 5 × 10−6). Among a sample of blazars at any particular cosmological epoch, those with lower γ-ray luminosity (L γ ) tend to have lower optical variability, and those FSRQs with a γ-ray photon index greater than 2.6 tend to have low L γ . We also measured the structure function of optical variability and found that the amplitude of the structure function for FSRQs is higher than previously measured and higher than that of BL Lacs at multiple time lags. Additionally, the amplitude of the structure function of FSRQs with high γ-ray photon index is significantly lower than that of FSRQs with low γ-ray photon index. The structure function of FSRQs of high γ-ray photon index shows a characteristic timescale of more than 10 days, which may be the variability timescale of the accretion disk. In summary, we infer that the optical component of FSRQs with high γ-ray photon index may be dominated by the accretion disk.

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Classification of the Fermi-LAT blazar candidates of uncertain type using extreme gradient boosting

Tolamatti

Singh

Yadav

2023

Monthly Notices of the Royal Astronomical Society

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Machine learning based approaches are emerging as very powerful tools for many applications including source classification in astrophysics research due to the availability of huge high quality data from different surveys in observational astronomy. The Large Area Telescope on board Fermi satellite (Fermi-LAT) has discovered more than 6500 high energy gamma-ray sources in the sky from its survey over a decade. A significant fraction of sources observed by the Fermi-LAT either remains unassociated or has been identified as Blazar Candidates of Uncertain type (BCUs). We explore the potential of eXtreme Gradient Boosting (XGBoost)- a supervised machine learning algorithm to identify the blazar subclasses among a sample of 112 BCUs of the 4FGL catalog whose X-ray counterparts are available within 95% uncertainty regions of the Fermi-LAT observations. We have used information from the multi-wavelength observations in IR, optical, UV, X-ray and γ-ray wavebands along with the redshift measurements reported in the literature for classification. Among the 112 uncertain type blazars, 62 are classified as BL Lacertae objects (BL Lacs) and 6 have been classified as Flat Spectrum Radio Quasars (FSRQs). This indicates a significant improvement with respect to the multi-perceptron neural network based classification reported in the literature. Our study suggests that the gamma-ray spectral index, and IR color indices are the most important features for identifying the blazar subclasses using the XGBoost classifier. We also explore the importance of redshift in the classification BCU candidates.

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Classifying Blazar Candidates from the 3FGL Unassociated Catalog into BL Lacertae Objects and Flat Spectrum Radio Quasars Using Swift and WISE Data

Cited by 5 publications

References 36 publications

Classification of Blazar Candidates of Unknown Type in Fermi 4LAC by Unanimous Voting from Multiple Machine-learning Algorithms

Classification of Blazar Candidates of Unknown Type in Fermi 4LAC by Unanimous Voting from Multiple Machine-learning Algorithms

Optical Variability of Blazars in the Tomo-e Gozen Northern Sky Transient Survey

Classification of the Fermi-LAT blazar candidates of uncertain type using extreme gradient boosting

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