A search for radio pulsars in five nearby supernova remnants
Bibliographic record
Abstract
Context.Most neutron stars are expected to be born in supernovae, but only about half of supernova remnants (SNRs) are associated with a compact object. In many cases, a supernova progenitor may have resulted in a black hole. However, there are several possible reasons why true pulsar-SNR associations may have been missed in previous surveys: The pulsar’s radio beam may not be oriented towards us; the pulsar may be too faint to be detectable; or there may be an offset in the pulsar position caused by a kick. Aims.Our goal is to find new pulsars in SNRs and explore their possible association with the remnant. The search and selection of the remnants presented in this paper was inspired by the non-detection of any X-ray bright compact objects in these remnants when previously studied. Methods.Five SNRs were searched for radio pulsars with the Green Bank Telescope at 820 MHz with multiple pointings to cover the full spatial extent of the remnants. A periodicity search plus an acceleration search up to 500 m s−2and a single pulse search were performed for each pointing in order to detect potential isolated binary pulsars and single pulses, respectively. Results.No new pulsars were detected in the survey. However, we were able to re-detect a known pulsar, PSR J2047+5029, near SNR G89.0+4.7. We were unable to detect the radio-quiet gamma-ray pulsar PSR J2021+4026, but we do find a flux density limit of 0.08 mJy. Our flux density limits make our survey two to 16 times more sensitive than previous surveys, while also covering the whole spatial extent of the same remnants. Conclusions.We discuss potential explanations for the non-detection of a pulsar in the studied SNRs and conclude that sensitivity is still the most likely factor responsible for the lack of pulsars in some remnants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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".