Bayesian analysis of single-pulse data from 1192 pulsars finds that roughly half require multi-component energy distributions and that nulling fractions increase with spin period.
The Thousand-Pulsar-Array programme on MeerKAT – I. Science objectives and first results
4 Pith papers cite this work, alongside 49 external citations. Polarity classification is still indexing.
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VLA L-band polarization observations of SNR G7.7-3.7 show cocoon morphology from interaction with pre-existing circumstellar shells, with magnetic fields compressed along filaments and RM variations tracing massive progenitor winds.
Pulsar radio emission beams from the two poles are generally dissimilar in azimuth width and often radius, based on rotating vector model fits to polarization data from eight double-pole interpulse pulsars.
LStein is presented as a novel visualization approach for sparse 2.5-dimensional data, implemented in Python and demonstrated on astronomical lightcurves.
citing papers explorer
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The Thousand-Pulsar-Array programme on MeerKAT XIX: Single-pulse data analysis, nulling and pulse energy distributions
Bayesian analysis of single-pulse data from 1192 pulsars finds that roughly half require multi-component energy distributions and that nulling fractions increase with spin period.
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The Cocoon from a Massive Star's Death: VLA Radio Polarization Study of Possible Historical Supernova Remnant G7.7$-$3.7
VLA L-band polarization observations of SNR G7.7-3.7 show cocoon morphology from interaction with pre-existing circumstellar shells, with magnetic fields compressed along filaments and RM variations tracing massive progenitor winds.
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On the Difference Between Pulsar Radio Emission Beams from the Two Poles
Pulsar radio emission beams from the two poles are generally dissimilar in azimuth width and often radius, based on rotating vector model fits to polarization data from eight double-pole interpulse pulsars.
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LStein: A new approach to visualizing sparse 2.5-dimensional data
LStein is presented as a novel visualization approach for sparse 2.5-dimensional data, implemented in Python and demonstrated on astronomical lightcurves.