Distances, Radial Distribution, and Total Number of Galactic Supernova Remnants
Bibliographic record
Abstract
Abstract We present a table of 215 supernova remnants (SNRs) with distances. New distances are found to SNR G51.26 + 0.11 of 6.6 ± 1.7 kpc using H i absorption spectra and to five other SNRs using maser/molecular cloud associations. We recalculate the distances and errors to all SNRs using a consistent rotation curve and provide errors where they were not previously estimated. This results in a significant distance revisions for 20 SNRs. Because of observational constraints and selection effects, there is an apparent deficit of observed number of Galactic SNRs. To investigate this, we employ two methods. The first method applies correction factors for the selection effects to derive the radial density distribution. The second method compares functional forms for the SNR surface density and selection function against the data to find which functions are consistent with the data. The total number of SNRs in the Galaxy is ∼3500 (Method 1) or in the range from ∼2400 to ∼5600 (Method 2). We conclude that the current observed number of SNRs is not yet complete enough to give a well-determined total SNR number or radial density function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".