TRAPUM search for pulsars in supernova remnants and pulsar wind nebulae – II. Survey analysis and population study
Bibliographic record
Abstract
ABSTRACT We present the second and final set of TRAPUM searches for pulsars at 1284 MHz inside supernova remnants and pulsar wind nebulae with the MeerKAT telescope. No new pulsars were detected for any of the 80 targets, which include some unidentified TeV sources that could be pulsar wind nebulae. The mean upper limit on the flux density of undetected pulsars is 52 $\mu$Jy, which includes the average sensitivity loss across the coherent beam tiling pattern. This survey is the largest and most sensitive multitarget campaign of its kind. We explore the selection effects that precluded discoveries by testing the parameters of the survey iteratively against many simulated populations of young pulsars in supernova remnants. For the synthetic pulsars that were undetected, we find evidence that, after beaming effects are accounted for, about 45 per cent of pulsars are too faint, 30 per cent are too smeared by scattering, and a further 25 per cent have a modelled projected location, which places them outside their supernova remnant. The simulations are repeated for the S1 subband of the MeerKAT S-band receivers, resulting in a 50–150 per cent increase in the number of discoveries compared to L band depending on the flux density limit achieved. Therefore, higher frequency searches that can also achieve improved flux density limits are the best hope for future targeted searches. We also report updated properties for the two previous discoveries, including a polarimetry study of PSR J1831–0941 finding a rotation measure of 401 $\pm$ 1 rad m$^{-2}$.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".