Classification of blazars based on data-driven approaches

Abstract

Active galactic nuclei (AGNs), including blazars, exhibit distinctive variability in their optical light curves, making them ideal for classification studies. This work uses data from the latest GAIA and Pan-STARRS data releases to analyze these patterns. The goal of this work is to classify AGNs into two categories: "blazars" and "non-blazars'' using only optical light curves. This strategy differs from most existing works, as it relies exclusively on optical variability without employing any other multiwavelength information. We processed optical light curves from GAIA and Pan-STARRS using the FATS library to extract standard time-series features. We computed additional features with custom algorithms based on literature methods. A Light Gradient-Boosting Machine (LightGBM) model was trained to classify AGNs into blazars and non-blazars based on these features. We then used this knowledge base to carry out a self-learning experiment with AGN candidates of an unknown nature. The LightGBM model achieved an accuracy of $86\%$, with precision, recall, and F1 score above $80-85\%$ for classifying blazars and non-blazar AGNs using optical data. The application of a BoostBoruta algorithm for feature selection reduced the feature space from 70 to 13. while maintaining comparable performance. A self-training classifier yielded similar results $85\%$, confirming the robustness of the model and the reliability of pseudo-labeling for unknown objects.

11 pages, 7 figures. Published online on Astronomy and Astrophysics on 03/07/2026

References in corpus (19)

• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• The Zwicky Transient Facility: System Overview, Performance, and First Results
• Multi-messenger observations of a flaring blazar coincident with high-energy neutrino IceCube-170922A
• A theoretical unifying scheme for gamma-ray bright blazars
• Roma-BZCAT: A multifrequency catalogue of Blazars
• The 5th edition of the Roma-BZCAT. A short presentation
• On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
• Selecting Quasars by their Intrinsic Variability
• Understanding extreme quasar optical variability with CRTS: I. Major AGN flares
• QSO Selection Algorithm Using Time Variability and Machine Learning: Selection of 1,620 QSO Candidates from MACHO LMC Database
• The EPOCH Project: I. Periodic variable stars in the EROS-2 LMC database
• Measuring X-ray variability in faint/sparsely-sampled AGN
• An improved quasar detection method in EROS-2 and MACHO LMC datasets
• An irregular discrete time series model to identify residuals with autocorrelation in astronomical light curves
• Gaia Data Release 3: The first Gaia catalogue of variable AGN
• Blazar variability with the Vera C. Rubin Legacy Survey of Space and Time (LSST)
• The variability of blazars throughout the electromagnetic spectrum
• Classification of blazar candidates of unknown type in Fermi 4LAC by unanimous voting from multiple Machine Learning Algorithms
• A Novel, Fully Automated Pipeline for Period Estimation in the EROS 2 Data Set