New optically identified supernova remnants in the Large Magellanic Cloud
Bibliographic record
Abstract
ABSTRACT We present a new optical sample of three Supernova Remnants (SNRs) and 16 Supernova Remnant (SNR) candidates in the Large Magellanic Cloud (LMC). These objects were originally selected using deep H α, [S ii], and [O iii] narrow-band imaging. Most of the newly found objects are located in less dense regions, near or around the edges of the LMC’s main body. Together with previously suggested MCSNR J0541–6659, we confirm the SNR nature for two additional new objects: MCSNR J0522–6740 and MCSNR J0542–7104. Spectroscopic follow-up observations for 12 of the LMC objects confirm high [S ii]/H α emission-line ratios ranging from 0.5 to 1.1. We consider the candidate J0509–6402 to be a special example of the remnant of a possible type Ia Supernova (SN) which is situated some 2° (∼1.75 kpc) north from the main body of the LMC. We also find that the SNR candidates in our sample are significantly larger in size than the currently known LMC SNRs by a factor of ∼2. This could potentially imply that we are discovering a previously unknown but predicted, older class of large LMC SNRs that are only visible optically. Finally, we suggest that most of these LMC SNRs are residing in a very rarefied environment towards the end of their evolutionary span where they become less visible to radio and X-ray telescopes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".