Classification of blazar candidates of unknown type in Fermi 4LAC by unanimous voting from multiple Machine Learning Algorithms

Abstract

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 Lacs), 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 Lacs 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, $\nu F\nu$ 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 Lacs 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 multi-wavelength surveys. This work can be further extended by adding features in X-rays, UV, visible, and radio wavelengths.

arXiv comment22 pages, 10 figures, 3 tables, Accepted in ApJ

References in corpus (41)

• SMOTE: Synthetic Minority Over-sampling Technique
• Unified Schemes for Radio-Loud Active Galactic Nuclei
• Fermi Large Area Telescope Fourth Source Catalog
• Fermi Large Area Telescope Second Source Catalog
• A Unifying View of the Spectral Energy Distributions of Blazars
• The Spectral Energy Distribution of Fermi bright blazars
• Leptonic and Hadronic Modeling of Fermi-Detected Blazars
• The Second Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope
• Incremental Fermi Large Area Telescope Fourth Source Catalog
• The Third Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope
• The First Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope
• The Fourth Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope
• The Fermi blazar sequence
• The Fourth Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope -- Data Release 3
• A Statistical Approach to Recognizing Source Classes for Unassociated Sources in the First Fermi-LAT Catalog
• The 3HSP catalogue of Extreme & High Synchrotron Peaked Blazars
• Fermi's Sibyl: Mining the gamma-ray sky for dark matter subhaloes
• 3FGL Demographics Outside the Galactic Plane using Supervised Machine Learning: Pulsar and Dark Matter Subhalo Interpretations
• Blazar Flaring Patterns (B-FlaP): Classifying Blazar Candidates of Uncertain type in the third Fermi-LAT catalog by Artificial Neural Networks
• Gamma-Ray Active Galactic Nucleus Type through Machine-Learning Algorithms
• Search for gamma-ray-emitting active galactic nuclei in the Fermi-LAT unassociated sample using machine learning
• Radio Galaxies at VHE energies
• Efficient Selection of Quasar Candidates Based on Optical and Infrared Photometric Data Using Machine Learning
• Research and characterisation of blazar candidates among the Fermi/LAT 3FGL catalogue using multivariate classifications
• 3FGLzoo. Classifying 3FGL Unassociated Fermi-LAT Gamma-ray Sources by Artificial Neural Networks
• Machine learning classification of CHIME fast radio bursts -- I. Supervised methods
• Evaluating the optical classification of Fermi BCUs using machine learning
• Optical spectroscopic survey of a sample of Unidentified Fermi objects: II
• Multi-band optical variability of 3C 279 on diverse timescales
• Classification of New X-ray Counterparts for Fermi Unassociated Gamma Ray Sources Using the Swift X-Ray Telescope
• Classification of Fermi-LAT unidentified gamma-ray sources using CatBoost gradient boosting decision trees
• Classification of Blazar Candidates of Uncertain Type from the Fermi LAT 8-Year Source Catalog with an Artificial Neural Network
• Multiwavelength Spectral Analysis and Neural Network Classification of Counterparts to 4FGL Unassociated Sources
• Multi-band behaviour of the TeV blazar PG 1553+113 in optical range on diverse timescales
• Evaluating the classification of Fermi BCUs from the 4FGL Catalog Using Machine Learning
• Searching for AGN and Pulsar Candidates in 4FGL Unassociated Sources Using Machine Learning
• Optimizing neural network techniques in classifying Fermi-LAT gamma-ray sources
• Optical Spectral Variability of the Very-High-Energy Gamma-Ray Blazar 1ES 1011+496
• Characterizing the optical nature of the blazar S5 1803+784 during its 2020 flare
• A search for dark matter among Fermi-LAT unidentified sources with systematic features in Machine Learning
• Classifying blazar candidates from the 3FGL unassociated catalog into BL Lacs and FSRQs using Swift and WISE data

Cited by in corpus (3)

• Classification of the \emph{Fermi}-LAT Blazar Candidates of Uncertain type using eXtreme Gradient Boosting
• Estimation of redshift and associated uncertainty of Fermi/LAT extra-galactic sources with Deep Learning
• Classification of blazars based on data-driven approaches