Two FRBs exhibit microlensing signatures consistent with intermediate-mass black holes of masses approximately 500-600 and 1500-2500 solar masses, interpreted as possible evidence for isolated primordial black holes comprising about 4% of dark matter.
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5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 5representative 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.
Four new FRBs discovered commensally during Parkes PTA pulsar observations, including one with record S/N and unusual spectrum; all highly polarized.
Frabjous applies deep learning to classify FRB morphologies into five classes at 55% accuracy by augmenting limited real data with simulations.
Author contributes to SPOTLIGHT collaboration using modern radio tech to search for fast radio transients and pulsars.
citing papers explorer
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Evidence for Intermediate-Mass Black Holes From Microlensing Signatures in CHIME/FRB catalog 2
Two FRBs exhibit microlensing signatures consistent with intermediate-mass black holes of masses approximately 500-600 and 1500-2500 solar masses, interpreted as possible evidence for isolated primordial black holes comprising about 4% of dark matter.
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A bright wideband radio burst from the isolated neutron star 2XMM J104608.7$-$594306
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.
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Commensal discovery of four Fast Radio Bursts during Parkes Pulsar Timing Array observations
Four new FRBs discovered commensally during Parkes PTA pulsar observations, including one with record S/N and unusual spectrum; all highly polarized.
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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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Searching For Fast Radio Transients And Radio Pulsars Using SPOTLIGHT
Author contributes to SPOTLIGHT collaboration using modern radio tech to search for fast radio transients and pulsars.