A causal machine-learning model using variability features from Fermi-LAT light curves predicts blazar flare activity within 90 days with 86% recall on held-out data for one FSRQ.
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Multi-instrument observations reveal broad overlap in X-ray photon indices across blazar subclasses with intra-source spectral evolution supporting transition-like behavior.
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Advance warning of $\gamma$-ray blazar flares from \textit{Fermi}-LAT light curves: a strictly causal machine-learning backtest
A causal machine-learning model using variability features from Fermi-LAT light curves predicts blazar flare activity within 90 days with 86% recall on held-out data for one FSRQ.
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Spectral-Regime Overlap and Transition-like Behavior in the Blazar Population from Multi-Instrument X-ray and TeV Observations
Multi-instrument observations reveal broad overlap in X-ray photon indices across blazar subclasses with intra-source spectral evolution supporting transition-like behavior.