CAN DUST EMISSION BE USED TO ESTIMATE THE MASS OF THE INTERSTELLAR MEDIUM IN GALAXIES—A PILOT PROJECT WITH THE HERSCHEL REFERENCE SURVEY
Bibliographic record
Abstract
The standard method for estimating the mass of the interstellar medium (ISM) in a galaxy is to use the 21 cm line to trace the atomic gas and the CO 1–0 line to trace the molecular gas. In this paper, we investigate the alternative technique of using the continuum dust emission to estimate the mass of gas in all phases of the ISM. Using Herschel observations of 10 galaxies from the Herschel Reference Survey and the Herschel Virgo Cluster Survey, we show that the emission detected by Herschel is mostly from dust that has a temperature and emissivity index similar to that of dust in the local ISM in our galaxy, with the temperature generally increasing toward the center of each galaxy. We calibrate the dust method using the CO and 21 cm observations to provide an independent estimate of the mass of hydrogen in each galaxy, solving the problem of the uncertain " X -factor" for the CO observations by minimizing the dispersion in the ratio of the masses estimated using the two methods. With the calibration for the dust method and the estimate of the X -factor produced in this way, the dispersion in the ratio of the two gas masses is 25%. The calibration we obtain for the dust method is similar to those obtained from Herschel observations of M31 and from Planck observations of the Milky Way. We discuss the practical problems in using this method.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".