Relativistic single-particle scattering cross sections for strong electromagnetic waves in strongly magnetized plasma are computed for arbitrary polarization and angle, showing strong suppression and sub-unity optical depth for quasi-parallel propagation.
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5 Pith papers cite this work, alongside 153 external citations. Polarity classification is still indexing.
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representative citing papers
PATH is extended with three fitted P(m_r|z) prior models combined with P(z|DM), raising host-association confidence for ASKAP FRBs while showing fainter-than-expected host magnitude distribution.
Wideband observations show M28A giant pulses differ from FRB 20200120E bursts in duration, luminosity, timing statistics, and spectral structure, yielding no strong evidence for a direct link.
Frabjous applies deep learning to classify FRB morphologies into five classes at 55% accuracy by augmenting limited real data with simulations.
The PINK updates enhance the CELEBI FRB pipeline with better astrometry, time-frequency gating, polarization calibration, DM optimization tools, and a software container for improved efficiency and localization of events like FRB 20251019A.
citing papers explorer
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Scattering of Strong Radio Waves by Particles in Strongly Magnetized Plasmas and Implications for Fast Radio Bursts
Relativistic single-particle scattering cross sections for strong electromagnetic waves in strongly magnetized plasma are computed for arbitrary polarization and angle, showing strong suppression and sub-unity optical depth for quasi-parallel propagation.
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Updating the PATH framework with FRB host galaxy models
PATH is extended with three fitted P(m_r|z) prior models combined with P(z|DM), raising host-association confidence for ASKAP FRBs while showing fainter-than-expected host magnitude distribution.
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Searching for links between energetic millisecond pulsars and repeating fast radio bursts
Wideband observations show M28A giant pulses differ from FRB 20200120E bursts in duration, luminosity, timing statistics, and spectral structure, yielding no strong evidence for a direct link.
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Frabjous: Deep Learning Fast Radio Burst Morphologies
Frabjous applies deep learning to classify FRB morphologies into five classes at 55% accuracy by augmenting limited real data with simulations.
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A PINK update: Improvements to the CELEBI fast radio burst data reduction and analysis pipeline
The PINK updates enhance the CELEBI FRB pipeline with better astrometry, time-frequency gating, polarization calibration, DM optimization tools, and a software container for improved efficiency and localization of events like FRB 20251019A.