An Automated Method for the Detection and Extraction of H<scp>i</scp>Self‐Absorption in High‐Resolution 21 Centimeter Line Surveys
Bibliographic record
Abstract
We describe algorithms that detect 21 cm line H I self-absorption (HISA) in large data sets and extract it for analysis. Our search method identifies HISA as spatially and spectrally confined dark H I features that appear as negative residuals after removing larger scale emission components with a modified CLEAN algorithm. Adjacent HISA volume-pixels (voxels) are grouped into features in ( l , b , v ) space, and the H I brightness of voxels outside the three-dimensional feature boundaries is smoothly interpolated to estimate the absorption amplitude and the unabsorbed H I emission brightness. The reliability and completeness of our HISA detection scheme have been tested extensively with model data. We detect most features over a wide range of sizes, line widths, amplitudes, and background levels, with poor detection only where the absorption brightness temperature amplitude is weak, the absorption scale approaches that of the correlated noise, or the background level is too faint for HISA to be distinguished reliably from emission gaps. False detection rates are very low in all parts of the parameter space except at sizes and amplitudes approaching those of noise fluctuations. Absorption measurement biases introduced by the method are generally small and appear to arise from cases of incomplete HISA detection. This paper is the third in a series examining HISA at high angular resolution. A companion paper (Paper II) uses our HISA search and extraction method to investigate the cold atomic gas distribution in the Canadian Galactic Plane Survey.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".