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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2 Pith papers cite this work, alongside 85 external citations. Polarity classification is still indexing.
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Estimates based on 17 years of Fermi LAT data indicate COSI may detect MeV polarization in a small number of blazar flares, with flat-spectrum radio quasars as the top targets.
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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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Detectability of Polarized Gamma-ray Emission from Blazar Flares with COSI
Estimates based on 17 years of Fermi LAT data indicate COSI may detect MeV polarization in a small number of blazar flares, with flat-spectrum radio quasars as the top targets.