The Search for Faint Radio Supernova Remnants in the Outer Galaxy: Five New Discoveries
Bibliographic record
Abstract
Context. High resolution and sensitivity large-scale radio surveys of the Milky Way are critical in the discovery of very low surface brightness supernova remnants (SNRs), which may constitute a significant portion of the Galactic SNRs still unaccounted for (osten-sibly the “Missing SNR problem”). Aims. The overall purpose here is to present the results of a systematic, deep data-mining of the Canadian Galactic Plane Survey (CGPS) for faint, extended non-thermal and polarized emission structures that are likely the shells of uncatalogued supernova rem-nants. Methods. We examine 5×5 degree mosaics from the entire 1420 MHz continuum and polarization dataset of the CGPS after re-moving unresolved “point ” sources and subsequently smoothing them. Newly revealed extended emission objects are compared to similarly-prepared CGPS 408 MHz continuum mosaics, as well as to source-removed mosaics from various existing radio surveys at 4.8 GHz, 2.7 GHz, and 327 MHz, to identify candidates with non-thermal emission characteristics. We integrate flux densities at each frequency to characterise the radio spectra behaviour of these candidates. We further look for mid- and high-frequency (1420 MHz, 4.8 GHz) ordered polarized emission from the limb brightened “shell”-like continuum features that the candidates sport. Finally, we use IR and optical maps to provide additional backing evidence. Results. Here we present evidence that five new objects, identified as filling all or some of the criteria above, are strong candidates for
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".