New ASKAP radio supernova remnants and candidates in the Large Magellanic Cloud
Bibliographic record
Abstract
ABSTRACT We present a new Australian Square Kilometre Array Pathfinder (ASKAP) sample of 14 radio supernova remnants (SNR) candidates in the Large Magellanic Cloud (LMC). This new sample is a significant increase to the known number of older, larger, and low surface brightness LMC SNRs. We employ a multifrequency search for each object and found possible traces of optical and occasionally X-ray emission in several of these 14 SNR candidates. One of these 14 SNR candidates (MCSNR J0522 – 6543) has multifrequency properties that strongly indicate a bona fide SNR. We also investigate a sample of 20 previously suggested LMC SNR candidates and confirm the SNR nature of MCSNR J0506 – 6815. We detect lower surface brightness SNR candidates which were likely formed by a combination of shock waves and strong stellar winds from massive progenitors (and possibly surrounding OB stars). Some of our new SNR candidates are also found in lower density environments in which SNe type Ia explode inside a previously excavated interstellar medium.
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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 | 0.001 | 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) | 0.003 | 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".