Updated radio $Σ-D$ relation for Galactic supernova remnants
Bibliographic record
Abstract
We present updated empirical radio surface-brightness-to-diameter ($Σ-D$) relation for supernova remnants (SNRs) in our Galaxy. Our original calibration sample of Galactic SNRs with independently determined distances (Pavlovic et al. 2013, hereafter Paper I) is reconsidered and updated with data which became available in the past two years. The orthogonal fitting procedure and probability-density-function-based (PDF) method are applied to the calibration sample in the $\log Σ- \log D$ plane. Orthogonal regression keeps $Σ-D$ and $D-Σ$ relations invariant. Our previous Monte Carlo simulations verified that the slopes of the empirical $Σ-D$ relation should be determined by using orthogonal regression. Updated calibration sample contains 65 shell SNRs. 6 new Galactic SNRs are added to the sample from Paper I, one is omitted and distances are changed for 10 SNRs. The slope derived here is slightly steeper ($β\approx 5.2$) than $Σ-D$ slope in Paper I ($β\approx 4.8$). The PDF method relies on data points density maps which can provide more reliable calibrations that preserve more information contained in the calibration sample. We estimate distances to five new faint Galactic SNRs discovered for the first time by Canadian Galactic Plane Survey and obtained distances of 2.3, 4.0, 1.3, 2.9 and 4.7 kiloparsecs for G108.5+11.0, G128.5+2.6, G149.5+3.2, G150.8+3.8 and G160.1$-$1.1, respectively. The updated empirical relation is used to estimate distances of 160 shell Galactic SNRs and new results change their distance scales up to 15 per cent, compared to results from Paper I. The PDF calculation can provide even few times higher or lower values in comparison with orthogonal fit, as it uses totally different approach. However, in average, this difference is 32, 24 and 18 per cent for mode, median and mean distances.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".