Classification of blazars based on data-driven approaches

arXiv:2607.08594 · astro-ph.GA, astro-ph.IM · Submitted 2026-07-09 · Read on arXiv

Simone Vaccaro, Maria Isabel Carnerero, Claudia M. Raiteri, Massimo Brescia, Ylenia Maruccia, Natale De Bonis, Giuseppe Riccio, Stefano Cavuoti

Department of Physics "E. Pancini'', University Federico II of Napoli, Napoli, Italy · INAF, Osservatorio Astrofisico di Torino, Pino Torinese, Italy · INAF, Osservatorio Astrofisico di Torino, Pino Torinese, Italy · INAF - Astronomical Observatory of Capodimonte, Napoli, Italy · INAF - Astronomical Observatory of Capodimonte, Napoli, Italy · Department of Physics "E. Pancini'', University Federico II of Napoli, Napoli, Italy · INAF - Astronomical Observatory of Capodimonte, Napoli, Italy · INFN section of Naples, Napoli, Italy

astro-ph.GA, astro-ph.IM

Submitted: 2026-07-09

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

Journal ref: A&A, 711, A50 (2026)

DOI: 10.1051/0004-6361/202659378

License: http://creativecommons.org/licenses/by/4.0/

The gist: Active galactic nuclei (AGNs), including blazars, exhibit distinctive variability in their optical light curves, making them ideal for classification studies.

Terminology

AGN — 49× in this paper · explained in 1 episode
Feature Selection — 17× in this paper · explained in 4 episodes
Accuracy — 14× in this paper · explained in 1 episode
Pseudo-labels — 9× in this paper · explained in 2 episodes
Robustness — 9× in this paper · explained in 5 episodes
F1 Score — 7× in this paper · explained in 3 episodes
SHAP values — 5× in this paper · explained in 2 episodes
Generalization — 4× in this paper · explained in 13 episodes
Pseudo-labeling — 4× in this paper · explained in 3 episodes
Damped Random Walk — 3× in this paper
Abundance — 2× in this paper · explained in 1 episode
Consistency — 2× in this paper · explained in 3 episodes
Core — 2× in this paper · explained in 2 episodes
AUC — 1× in this paper · explained in 2 episodes
Adam — 1× in this paper · explained in 2 episodes
Feature Analysis for Time Series — 1× in this paper
Information Gain — 1× in this paper · explained in 2 episodes
Loss Function — 1× in this paper · explained in 2 episodes
Semi-supervised Learning — 1× in this paper · explained in 4 episodes
Structure Function — 1× in this paper

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.

Sources

FATS: Feature Analysis for Time Series

Terminology

Abstract

Sources

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