CHIME/FRB has now cataloged 80 repeating FRB sources whose burst rates and upper limits are consistent with a power-law distribution implying 50-100% of all FRBs repeat.
Title resolution pending
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
representative citing papers
Analysis of CHIME/FRB Catalog 2 with synthetic injections and a multidimensional selection function yields evidence for a slight downturn in the intrinsic scattering timescale distribution, though flat or rising distributions remain possible.
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.
The BURSTT back-end system achieves real-time multi-stage beamforming and de-dispersion search to detect FRBs and trigger VLBI localization using RFSoC and optimized server processing.
Frabjous applies deep learning to classify FRB morphologies into five classes at 55% accuracy by augmenting limited real data with simulations.
A reported periodic fast radio burst is reclassified as Galactic pulsar emission due to CHIME calibration and beam-pointing error.
citing papers explorer
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Back-End System of BURSTT
The BURSTT back-end system achieves real-time multi-stage beamforming and de-dispersion search to detect FRBs and trigger VLBI localization using RFSoC and optimized server processing.
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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.