Automatic Detection of Expanding H<scp>i</scp>Shells in the Canadian Galactic Plane Survey Data
Bibliographic record
Abstract
The identification of expanding H I shells is difficult because of their variable morphology. In this paper we present an automatic detector for H I shells, based on the more stable dynamical characteristics of expanding bubbles with radii <40 pc. The detection is performed in two stages. First, artificial neural networks are trained to recognize the dynamical signature of an expanding bubble in the velocity spectra of 21 cm data. The second stage consists of subsequent validations based on the potential bubble's morphology. The technique is tested on 11 known bubbles, and 10 of them are successfully detected. Conducting a systematic detection on a 48° × 9° region in the Perseus arm, we obtain 7100 detections with spatial distribution following the stellar distribution of the Galactic disk. The estimated radius and expansion velocity distributions for objects with R ≤ 10 pc agree with the distributions predicted by models of adiabatically expanding bubble populations. The fraction of the Perseus arm volume occupied by the detected objects, which can be interpreted as the small bubbles' contribution to the Galactic porosity Q , is calculated to Q R <40 pc = 0.007 . Four new bubble cases and eight serious candidates, related to known progenitors, are proposed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".