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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2508.20927 |
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| _version_ | 1866915468425560064 |
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| author | Cerruti, Matteo |
| author_facet | Cerruti, Matteo |
| contents | Blazars dominate the extragalactic $γ$-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 $γ$-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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20927 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unsupervised Classification of Gamma-ray Bursts from Blazars (GRBBLs) with Machine Learning Cerruti, Matteo High Energy Astrophysical Phenomena Blazars dominate the extragalactic $γ$-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 $γ$-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. |
| title | Unsupervised Classification of Gamma-ray Bursts from Blazars (GRBBLs) with Machine Learning |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2508.20927 |