Mapping a Lower Limit on the Mass Fraction of the Cold Neutral Medium Using Fourier-transformed H i 21 cm Emission Line Spectra: Application to the DRAO Deep Field from DHIGLS and the HI4PI Survey
Bibliographic record
Abstract
Abstract We develop a new method for spatially mapping a lower limit on the mass fraction of the cold neutral medium by analyzing the amplitude structure of T ˆ b ( k v ) , the Fourier transform of T b (v), the spectrum of the brightness temperature of the H i 21 cm line emission with respect to the radial velocity v. This advances a broader effort exploiting 21 cm emission line data alone (without absorption line data, τ) to extract integrated properties of the multiphase structure of the H i gas and to map each phase separately. Using toy models, we illustrate the origin of interference patterns seen in T ˆ b ( k v ) . Building on this, a lower limit on the cold gas mass fraction is obtained from the amplitude of T ˆ b at high k v . Tested on a numerical simulation of thermally bi-stable turbulence, the lower limit from this method has a strong linear correlation with the “true” cold gas mass fraction from the simulation for a relatively low cold gas mass fraction. At a higher mass fraction, our lower limit is lower than the “true” value, because of a combination of interference and opacity effects. Comparison with absorption surveys shows a similar behavior, with a departure from linear correlation at N H I ≳ 3–5 × 1020 cm−2. Application to the DRAO Deep Field from DHIGLS reveals a complex network of cold filaments in the Spider, an important structural property of the thermal condensation of the H i gas. Application to the HI4PI survey in the velocity range −90 < v < 90 km s−1 produces a full sky map of a lower limit on the mass fraction of the cold neutral medium at 16.′2 resolution. Our new method has the ability to extract a lower limit on the cold gas mass fraction for massive amounts of emission line data alone with low computing time and memory, pointing the way to new approaches suitable for the new generation of radio interferometers.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".