Numerical transport modeling of the Cygnus Bubble finds that spatially dependent Bohm diffusion and strong suppression of the diffusion coefficient over at least 150 pc are required to match the observed gamma-ray spectrum and morphology, implying extreme assumptions for steady hadronic acceleration
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2026 2verdicts
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This review describes the IACT event reconstruction pipeline and the role of machine learning for classification and regression, highlighting timing features and ensemble methods as improvements over baseline approaches.
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Suppressed diffusion and gamma-ray emission from the Cygnus Bubble
Numerical transport modeling of the Cygnus Bubble finds that spatially dependent Bohm diffusion and strong suppression of the diffusion coefficient over at least 150 pc are required to match the observed gamma-ray spectrum and morphology, implying extreme assumptions for steady hadronic acceleration
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Machine Learning for Event Reconstruction in Imaging Atmospheric Cherenkov Telescopes
This review describes the IACT event reconstruction pipeline and the role of machine learning for classification and regression, highlighting timing features and ensemble methods as improvements over baseline approaches.