TRAPUM search for pulsars in supernova remnants and pulsar wind nebulae − I. Survey description and initial discoveries
Bibliographic record
Abstract
ABSTRACT We present the description and initial results of the TRAPUM (TRAnsients And PUlsars with MeerKAT) search for pulsars associated with supernova remnants (SNRs), pulsar wind nebulae, and unidentified TeV emission. The list of sources to be targeted includes a large number of well-known candidate pulsar locations but also new candidate SNRs identified using a range of criteria. Using the 64-dish MeerKAT radio telescope, we use an interferometric beamforming technique to tile the potential pulsar locations with coherent beams which we search for radio pulsations, above a signal-to-noise of 9, down to an average flux density upper limit of 30 μJy. This limit is target-dependent due to the contribution of the sky and nebula to the system temperature. Coherent beams are arranged to overlap at their 50 per cent power radius, so the sensitivity to pulsars is not degraded by more than this amount, though realistically averages around 65 per cent if every location in the beam is considered. We report the discovery of two new pulsars; PSR J1831−0941 is an adolescent pulsar likely to be the plerionic engine of the candidate PWN G20.0+0.0, and PSR J1818−1502 appears to be an old and faint pulsar that we serendipitously discovered near the centre of a SNR already hosting a compact central object. The survey holds importance for better understanding of neutron star birth rates and the energetics of young pulsars.
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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 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.002 | 0.001 |
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".