The Fourth Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope: Data Release 3

Ajello, M.; Baldini, Luca; Ballet, J.; Bastieri, D.; González, J. Becerra; Bellazzini, R.; Berretta, A.; Bissaldi, E.; Bonino, R.; Brill, A.; Bruel, P.; Buson, S.; Caputo, R.; Caraveo, P. A.; Cheung, C. C.; Chiaro, G.; Cibrario, N.; Ciprini, S.; Crnogorcevic, M.; Cutini, S.; D’Ammando, F.; Gaetano, S. De; Lalla, N. Di; Venere, L. Di; Domínguez, A.; Ramazani, V. Fallah; Ferrara, E. C.; Fiori, A.; Fukazawa, Yasushi; Funk, S.; Fusco, P.; Gammaldi, Viviana; Gargano, F.; Garrappa, S.; Gasparrini, D.; Giglietto, N.; Giordano, F.; Giroletti, M.; Green, D.; Grenier, I. A.; Guiriec, S.; Horan, D.; Hou, X.; Kayanoki, Taishu; Kuss, M.; Larsson, Stefan; Latronico, L.; Lewis, Tiffany R.; Li, Jian; Liodakis, Ioannis; Longo, F.; Loparco, F.; Lott, B.; Lovellette, M. N.; Lubrano, P.; Madejski, G. M.; Maldera, S.; Manfreda, Alberto; Martí-Devesa, G.; Mazziotta, M. N.; Mereu, I.; Michelson, P. F.; Mirabal, N.; Mitthumsiri, W.; Mizuno, T.; Monzani, M. E.; Morselli, A.; Moskalenko, I. V.; Negro, Michela; Ojha, R.; Orienti, M.; Orlando, Elena; Ormes, J. F.; Pei, Z. Y.; Peña-Herazo, H. A.; Peršić, M.; Pesce-Rollins, M.; Petrosian, V.; Pillera, Roberta; Poon, H.; Porter, T. A.; Principe, G.; Rainò, S.; Rando, R.; Rani, Bindu; Razzano, M.; Razzaque, S.; Reimer, A.; Reimer, O.; Scotton, L.; Serini, D.; Sgrò, C.; Siskind, E. J.; Spandre, G.; Spinelli, P.; Suson, D. J.; Tajima, H.; Torres, D. F.; Valverde, J.; Yassin, Hend Mahmoud; Zaharijaš, Gabrijela

doi:10.3847/1538-4365/ac9523

Cited by 58 publications

(37 citation statements)

References 35 publications

(44 reference statements)

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“…For a more detailed understanding, we refer the reader to Ajello et al (2022) and references therein. The spectral analysis in 4LAC-DR3 (Ajello et al 2022) has been done following a similar procedure as described in 4FGL-DR1 (Abdollahi et al 2020) except that now a different parameterization is being used for pulsars, a greater number of sources are fit, the threshold for considering spectral curvature as significant has been lowered, a new column with peak energy in ν F ν has been reported, and a spectral bin has been added to SEDs. The spectral representation of sources still follows a power law, a power law with subexponential cutoff, and a log-normal.…”

Section: Sample Selectionmentioning

confidence: 99%

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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: Sample Selectionmentioning

confidence: 99%

“…The distribution of fractional variability in DR3 was found to be similar to DR1 peaking between 50% and 90%. All parameters used in 4LAC-DR3 are listed in Table A1 of Ajello et al (2022).…”

Section: Sample Selectionmentioning

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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“…These studies led to the classification of J1419−0838 as an FSRQ, without actually investigating whether its radio spectrum is flat or not. This new classification is also reflected in the Fermi-LAT Fourth AGN Catalog (4LAC, Ajello et al 2022). However, these works do not take the radio properties into account in the classification process and the analysis of the source.…”

Section: Introductionmentioning

confidence: 82%

Radio and mid-infrared properties of the blazar J1419–0838

Perger

Frey

Gabányi

2023

Astrophys Space Sci

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The radio quasar NVSS J141922−083830 (J1419−0838) was initially classified as an uncategorised blazar-type object, following its detection in the $\gamma $ γ -ray band with the Fermi space telescope. Later, using multi-waveband observations and modeling, its was found to be a flat-spectrum radio quasar (FSRQ). However, its radio emission has never been discussed in depth in the literature. Here we present a detailed analysis on the radio properties of J1419−0838 using archival interferometric imaging data at pc and kpc scales. We conclude that the flux density variations, the flat radio spectrum, the compact nature of the quasar structure at all scales, and the relativistic Doppler enhancement of the radio emission all support the previous classification as an FSRQ. We also investigated the short- and long-term mid-infrared (MIR) light curve of the quasar based on observations by the Wide-field Infrared Survey Explorer satellite, and found that there is significant variability on time-scales of days as well as years. Comparison of the MIR light curve to the times of previously reported $\gamma $ γ -ray and optical flares shows no clear correlation between the events at different wavebands.

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“…The Fermi-LAT fourth catalog of AGNs (4LAC) was released in 2019, spanning 8 years of LAT data (2008-2018) Ajello et al (2020). A second release, 4LAC-DR2, was published two years later with two additional years of data Lott et al (2020), while a third one -and latest at this writing-, 4FGL-DR3, was published in 2022 covering 12 years of LAT observations Ajello et al (2022).…”

Section: The 4lac-dr3 Catalogmentioning

confidence: 99%

“…In a previous paper, we used CatBoost 5 for a multiclass classification of unIDs Coronado-Blázquez (2022). In this paper, we aim to use it for redshift prediction using the latest 4LAC-DR3 catalog Ajello et al (2022) (built on the LAT 12-year 4FGL-DR3 source catalog Abdollahi et al (2022)) to train the algorithm with the known-redshift sample of AGNs, and then predict the redshift of the remaining objects. In a second part, we will predict the redshift of the unassociated sample of the 4FGL-DR3 catalog.…”

mentioning

confidence: 99%

Classification of Fermi-LAT unidentified gamma-ray sources using catboost gradient boosting decision trees

Coronado-Blázquez

2022

Monthly Notices of the Royal Astronomical Society

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The latest Fermi-LAT gamma-ray catalogue, 4FGL-DR3, presents a large fraction of sources without clear association to known counterparts, i.e. unidentified sources (unIDs). In this paper, we aim to classify them using machine learning algorithms, which are trained with the spectral characteristics of associated sources to predict the class of the unID population. With the state-of-the-art catboost algorithm, based on gradient boosting decision trees, we are able to reach a 67 per cent accuracy on a 23-class data set. Removing a single of these classes – blazars of uncertain type – increases the accuracy to 81 per cent. If interested only in a binary AGN/pulsar distinction, the model accuracy is boosted up to 99 per cent. Additionally, we perform an unsupervised search among both known and unID population, and try to predict the number of clusters of similar sources, without prior knowledge of their classes. The full code used to perform all calculations is provided as an interactive python notebook.

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The Fourth Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope: Data Release 3

Cited by 58 publications

References 35 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

Radio and mid-infrared properties of the blazar J1419–0838

Classification of Fermi-LAT unidentified gamma-ray sources using catboost gradient boosting decision trees

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