FRB 20240114A shows a ~112-day periodic modulation in central emission frequency with systematic upward drift within each period at >6σ significance.
Title resolution pending
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
representative citing papers
A second coherent radio burst spanning 704-4032 MHz with spectral index -2.18, 54% linear and 22% circular polarization, and an orthogonal polarization angle jump was detected from 2XMM J104608.7-594306, showing rare radio activity in sources thought to be radio-quiet.
A polarization catalog of 6,131 bursts from FRB 20240114A reveals linearly decreasing rotation measure, stable dispersion measure, high linear polarization fractions, low circular polarization, and a broad distribution of intrinsic polarization angles, indicating a dynamically evolving magneto-ionic
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.
Polarization position angles of repeating FRBs are Gaussian distributed with no periodicity, arising from geometric projection in a stochastically varying magnetosphere that also explains non-repeating FRBs.
A search of repeating FRBs identifies RM flare candidates in FRB 20121102A, FRB 20201124A, and FRB 20180916B, suggesting such events may be common and tied to dynamic magneto-ionic environments.
Wave mode coupling reproduces diverse frequency evolutions of circular polarization in pulsar single pulses, constraining multiplicity to 1-100 and Lorentz factor to 3-100, though the model fails to quantitatively fit most observed spectra.
Frabjous applies deep learning to classify FRB morphologies into five classes at 55% accuracy by augmenting limited real data with simulations.
citing papers explorer
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On the Diversity of Pulsar's Frequency-Dependent Circular Polarization
Wave mode coupling reproduces diverse frequency evolutions of circular polarization in pulsar single pulses, constraining multiplicity to 1-100 and Lorentz factor to 3-100, though the model fails to quantitatively fit most observed spectra.
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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.