Reprocessing of the Parkes 70 cm Survey and Discovery of a New Radio Pulsar in the Large Magellanic Cloud
Bibliographic record
Abstract
Abstract We have reprocessed the data archived from the Parkes 70 cm (PKS70) pulsar survey with an expanded dispersion measure (DM) search range and an acceleration search. Our goal was to detect pulsars that might have been missed in the original survey processing. Of the original 43,842 pointings, 34,869 pointings were archived, along with 440 additional pointings for confirmation or timing. We processed all of these archived data and detected 359 known pulsars: 265 of these were detected in the original survey, while an additional 94 currently known pulsars were detected in our reprocessing. A few among those 94 pulsars are highly accelerated binary pulsars. Furthermore, we detected 5 more pulsars with DMs higher than the original survey thresholds, as well as 6 more pulsars below the nominal survey sensitivity threshold (from the original survey beams with longer integrations). We missed detection of 33 (of the 298) pulsars detected in the original survey, in part because of portions of the survey data missing in the archive and our early-stage candidate sifting method. We discovered one new pulsar in the reanalysis, PSR J0540−69, which has a spin period of 0.909 s and resides in the Large Magellanic Cloud (LMC). This new pulsar appeared in three PKS70 beams and one additional L-band observation that targeted the LMC pulsar PSR B0540−69. The numerous pulsar detections found in our reanalysis and the discovery of a new pulsar in the LMC highlight the value of conducting multiple searches through pulsar data sets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".