Considerations with stacking absorption spectra: cold H <scp>i</scp> gas in cirrus region of the Milky Way
Bibliographic record
Abstract
ABSTRACT We use the Milky Way neutral hydrogen (H i) absorption and emission spectra from the Galactic Australian Square Kilometre Array Pathfinder (GASKAP) Phase II Pilot survey along with toy models to investigate the effects of stacking multicomponent spectra on measurements of peak optical depth and spin temperature. Shifting spectra by the peak in emission, ‘primary’ components shifted to 0 km s$^{-1}$ are correctly averaged. Additional components on individual sightlines are averaged with non-centred velocities, producing a broader and shallower ‘secondary’ component in the resulting stack. Peak optical depths and brightness temperatures of the secondary components from stacks are lower limits of their true average values due to the velocity offset of each component. The spin temperature however is well correlated with the truth since the velocity offset of components affects the emission and absorption spectra equally. Stacking 462 GASKAP absorption-emission spectral pairs, we detect a component with a spin temperature of $1320 \pm 263$ K, consistent with gas from the unstable neutral medium and higher than any previous GASKAP detection in this region. We also stack 2240 pilot survey spectra containing no Milky Way absorption, revealing a primary narrow and secondary broad component, with spin temperatures belonging to the cold neutral medium (CNM). Spatially binning and stacking the non-detections across the plane-of-sky by their distance from CNM absorption detections, the primary component’s optical depth decreases with distance from known locations of cold gas. The spin temperature however remains stable in both components, over an approximate physical plane-of-sky distance of $\sim 100$ pc.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".