A First Look at Spatially Resolved Infrared Supernova Remnants in M33 with JWST
Bibliographic record
Abstract
Abstract We present the first spatially resolved infrared images of supernova remnants (SNRs) in M33 with the unprecedented sensitivity and resolution of JWST. We analyze 40 SNRs in four JWST fields: two covering central and southern M33 with separate NIRCam (F335M, F444W) and MIRI (F560W, F2100W) observations, one ∼5 kpc-long radial strip observed with MIRI F770W, and one covering the giant H II region NGC 604 with multiple NIRCam and MIRI broad/narrowband filters. Of the 21 SNRs in the MIRI (F560W+F2100W) field, we found three clear detections (i.e., identical infrared and Hα morphologies), and six partial-detections, implying a detection fraction of 43% in these bands. One of the SNRs in this field, L10-080, is a potential candidate for having freshly formed ejecta dust, based on its size and centrally concentrated 21 μm emission. In contrast, only one SNR (out of 16) is detectable in the NIRCam F335M+F444W field. Two SNRs near NGC 604 have strong evidence of molecular (H2) emission at 4.7 μm, making them the farthest known SNRs with visible molecular shocks. Five SNRs have F770W observations, with the smaller younger objects showing tentative signs of emission, while the older, larger ones have voids. Multiwavelength data indicate that the clearly detected SNRs are also among the smallest, brightest at other wavelengths (Hα, radio, and X-ray), have the broadest line widths (Hα FWHM ∼ 250–350 km s−1), and the densest environments. No correlation between the JWST-detectability and local star formation history of the SNRs is apparent.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".