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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An accelerating plasma blob crossing the BLR in 3C 279 reproduces the 2013 orphan gamma-ray flare's hard spectrum, rapid rise, slow decay, and lack of optical variability via varying external Compton scattering.
First results from the SPOTS campaign reveal low average optical polarization (≲10%) and low magnetic field ordering (F_B ≲0.10) across 14 TeV blazars, with stochastic or rotating polarization angles and wavelength-dependent behavior indicating complex, turbulent jet structures.
citing papers explorer
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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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Blazar flares from plasma blobs crossing the broad-line region
An accelerating plasma blob crossing the BLR in 3C 279 reproduces the 2013 orphan gamma-ray flare's hard spectrum, rapid rise, slow decay, and lack of optical variability via varying external Compton scattering.
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Spectro-Polarimetric Observations of TeV Sources (SPOTS): First results
First results from the SPOTS campaign reveal low average optical polarization (≲10%) and low magnetic field ordering (F_B ≲0.10) across 14 TeV blazars, with stochastic or rotating polarization angles and wavelength-dependent behavior indicating complex, turbulent jet structures.