Early Detection of Multiwavelength Blazar Variability

TL;DR

This paper introduces a deep learning framework for real-time detection of blazar flares across multiple wavelengths, enhancing the ability to trigger observations and study high-energy astrophysical phenomena.

Contribution

It presents a novel unsupervised anomaly detection method that identifies blazar flares without requiring labeled training data, handling data uncertainties and observational gaps.

Findings

Successfully detects historical blazar flares in real data

Effective in differentiating source variability from noise

Robust across different data qualities and observational cadences

Abstract

Blazars are a subclass of active galactic nuclei with relativistic jets pointing toward the observer. They are notable for their flux variability at all observed wavelengths and timescales. Together with simultaneous measurements at lower energies, the very-high-energy (VHE) emission observed during blazar flares may be used to probe the population of accelerated particles. However, optimally triggering observations of blazar high states can be challenging. Notable examples include identifying a flaring episode in real time and predicting VHE flaring activity based on lower-energy observables. For this purpose, we have developed a novel deep learning analysis framework, based on data-driven anomaly detection techniques. It is capable of detecting various types of anomalies in real-world, multiwavelength light curves, ranging from clear high states to subtle correlations across bands. Based on unsupervised anomaly detection and clustering methods, we differentiate source variability from noisy background activity, without the need for a labeled training data set of flaring states. The framework incorporates measurement uncertainties and is robust given data quality challenges, such as varying cadences and observational gaps. We evaluate our approach using both historical data and simulations of blazar light curves in two energy bands, corresponding to sources observable with the Fermi Large Area Telescope and the upcoming Cherenkov Telescope Array Observatory. In a statistical analysis, we show that our framework can reliably detect known historical flares.