Multiwavelength probes of the Milky Way’s cold interstellar medium: radio H <scp>i</scp> and optical K <scp>i</scp> absorption with GASKAP and GALAH
Bibliographic record
Abstract
ABSTRACT We present a comparative analysis of interstellar hydrogen (H i) and potassium (K i) absorption from the radio and optical surveys, GASKAP (Galactic Australian Square Kilometre Array Pathfinder) and GALAH (Galactic Archaeology with HERMES), to study the physical and kinematic properties of the cold interstellar medium (ISM) in the Milky Way foreground towards the Magellanic Clouds. By comparing GASKAP H i absorption with interstellar K i absorption detected in GALAH spectra of nearby stars (within 12 arcmin angular distance or a spatial separation of $\sim$0.75 pc), we reveal a strong kinematic correlation between these two tracers of the cold neutral ISM. The velocity offsets between matched H i and K i absorption components are small, with a mean (median) offset of –1.3 (–1.2) $\mathrm{km\, s^{-1}}$ and a standard deviation of 2.3 $\mathrm{km\, s^{-1}}$. The high degree of kinematic consistency suggests a close spatial association between K i and cold H i gas. Correlation analyses reveal a moderate positive relationship between H i and K i line-of-sight properties, such as K i column density with H i column density or H i brightness temperature. We observe a $\sim$63 per cent overlap in the detection of both species towards 290 (out of 462) GASKAP H i absorption lines of sight, and estimate a median K i/H i abundance ratio of $\sim 2.3\times 10^{-10}$, in excellent agreement with previous findings. Our work opens up an exciting avenue of Galactic research that uses large-scale surveys in the radio and optical wavelengths to probe the neutral ISM through its diverse tracers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".