Glitches in southern pulsars
Bibliographic record
Abstract
Timing observations of 40 mostly young pulsars using the ATNF Parkes radio telescope between 1990 January and 1998 December are reported. In total, 20 previously unreported glitches and 10 other glitches were detected in 11 pulsars. These included 12 glitches in PSR J1341-6220, corresponding to a glitch rate of 1.5 glitches per year. We also detected the largest known glitch, in PSR J1614-5047, with Deltanugnu~6.5×10-6, where nu=1/P is the pulse frequency. Glitch parameters were determined both by extrapolating timing solutions to interglitch intervals and by phase-coherent timing fits across the glitch(es). These fits also give improved positions and dispersion measures for many of the pulsars. Analysis of glitch parameters, both from this work and from previously published results, shows that most glitches have a fractional amplitude Deltanugnu of between 10-8 and 10-6. There is no consistent relationship between glitch amplitude and the time since the previous glitch or the time to the following glitch, either for the ensemble or for individual pulsars. As previously recognized, the largest glitch activity is seen in pulsars with ages of order 104yr, but for about 30per cent of such pulsars, no glitches were detected in the 8-year data span. There is some evidence for a new type of timing irregularity in which there is a significant increase in pulse frequency over a few days, accompanied by a decrease in the magnitude of the slow-down rate. Fits of an exponential recovery to post-glitch data show that for most older pulsars, only a small fraction of the glitch decays. In some younger pulsars a large fraction of the glitch decays, but in others there is very little decay. Apart from the Crab pulsar, there is no clear dependence of recovery time-scale on pulsar age.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".