A Catalog of Galactic Supernova Remnants and Supernova Remnant Candidates from the EMU/POSSUM Radio Sky Surveys. I.
Bibliographic record
Abstract
Abstract We use data from the Evolutionary Map of the Universe (EMU) and Polarization Sky Survey of the Universe’s Magnetism (POSSUM) radio southern sky surveys, conducted with the Australian Square Kilometre Array Pathfinder (ASKAP) to compile a catalog of Galactic supernova remnants (SNRs) and candidate SNRs within the region of 277 . ° 5 ≤ ℓ ≤ 311 . ° 7 Galactic longitude, ∣ b ∣ ≤ 5 . ° 4 Galactic latitude, as well as an additional field along the Galactic plane, approximately 315 . ° 5 ≤ ℓ ≤ 323 . ° 0 Galactic longitude, −4.5 ≤ b ≤ 1.5 Galactic latitude. In the areas studied, there are 44 known SNRs and 46 SNR candidates that have been previously identified in the radio. We confirm eight of these candidates as SNRs based on evidence of linear polarization or through the calculation of nonthermal spectral indices. Additionally, we identify possible radio counterparts for seven SNR candidates that were previously only identified in X-rays (four) or optical (three). We also present six new SNRs and 37 new SNR candidates. The results of this study demonstrate the utility of ASKAP for discovering new and potential SNRs and refining the classification of previously identified candidates. In particular, we find that the EMU and POSSUM surveys are particularly well suited for observing high-latitude SNRs and confirming SNR candidates with polarization. The region studied in this work represents approximately one-quarter of the Galactic plane, by longitude, that will eventually be surveyed by EMU/POSSUM, and we expect that the ongoing surveys will continue to uncover new SNRs and SNR candidates.
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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 | 0.009 | 0.008 |
| 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) | 0.009 | 0.006 |
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".