# Unsupervised Classification of Gamma-ray Bursts from Blazars (GRBBLs) with Machine Learning

## TL;DR

This paper develops an unsupervised machine learning pipeline to classify gamma-ray bursts from blazars using Fermi-LAT data, revealing population homogeneity and correlations between luminosity and timescales.

## Contribution

It introduces a novel automated method for identifying and classifying GRBBLs, including a new luminosity-driven classification approach based on spectral variability.

## Key findings

- GRBBL population is highly homogeneous.
- Achromatic vs. chromatic event classification is most robust.
- A correlation between peak luminosity and timescales in GRBBLs was identified.

## Abstract

Blazars dominate the extragalactic $\gamma$-ray sky and show pronounced flares. Using public Fermi-LAT light curves for 732 blazars with secure redshifts, I implement an automated pipeline to identify and characterize $\gamma$-ray bursts from blazars (GRBBLs). Each event is modeled with an exponential rise/decay profile, and spectral variability is quantified via a constant fit. From 679 high-quality GRBBLs, I apply extreme deconvolution for unsupervised classification. The GRBBL population is remarkably homogeneous; the most robust split is in achromatic vs. chromatic events, with significant overlap. Removing spectral information yields a luminosity-driven classification in type-1 and type-2 GRBBLs, although this classification is not identified in all tests. This study establishes GRBBL population studies as a tool to study blazars. As a by-product of this project I identify a correlation between peak luminosity and timescales in GRBBLs.