THE MIPSGAL VIEW OF SUPERNOVA REMNANTS IN THE GALACTIC PLANE
Bibliographic record
Abstract
We report the detection of Galactic supernova remnants (SNRs) in the mid-infrared (at 24 and 70 μm), in the coordinate ranges 10° < l < 65° and 285° < l < 350°, | b | < 1°, using MIPS aboard the Spitzer Space Telescope . We search for infrared counterparts to SNRs in Green's catalog and identify 39 out of 121, i.e., a detection rate of about 32%. Such a relatively low detection fraction is mainly due to confusion with nearby foreground/background sources and diffuse emission. The SNRs in our sample show a linear trend in [ F 8 / F 24 ] versus [ F 70 / F 24 ]. We compare their infrared fluxes with their corresponding radio flux at 1.4 GHz and find that most remnants have a ratio of 70 μm to 1.4 GHz which is similar to those found in previous studies of SNRs (with the exception of a few that have ratios closer to those of H ii regions). Furthermore, we retrieve a slope close to unity when correlating infrared (24 and 70 μm) with 1.4 GHz emission. Our survey is more successful in detecting remnants with bright X-ray emission, which we find is well correlated with the 24 μm morphology. Moreover, by comparing the power emitted in the X-ray, infrared, and radio, we conclude that the energy released in the infrared is comparable to the cooling in the X-ray range.
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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 | 0.003 | 0.001 |
| 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) | 0.005 | 0.001 |
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".