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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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.
Plasmoid reconnection simulations match observed skewness transitions and entropy decreases in FSRQ GeV light curves after flares, with broken power-law PSDs consistent with blazar variability.
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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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.
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Reconnection-driven State Transitions in Flat Spectrum Radio Quasars
Plasmoid reconnection simulations match observed skewness transitions and entropy decreases in FSRQ GeV light curves after flares, with broken power-law PSDs consistent with blazar variability.