A NEW METHOD FOR OBTAINING THE STAR FORMATION LAW IN GALAXIES
Bibliographic record
Abstract
We present a new observational method to evaluate the exponent of the star formation law as initially formulated by Schmidt, i.e., the power-law expression assumed to relate the rate of star formation in a volume of space to the local total gas volume density present there. Total volume densities in the gas clouds surrounding an OB association are determined with a simple model which considers the atomic hydrogen as a photodissociation product on the cloud surfaces. The photodissociating photon flux incident on the cloud is computed from the far-UV luminosity of the OB association and the geometry. As an example, we have applied this "PDR Method" to a sample of star-forming regions in M33 using Very Large Array (VLA) 21 cm data for the H i and Galaxy Evolution Explorer ( GALEX ) imagery in the far-UV. With these two observables, our approach provides an estimate of the total volume density of hydrogen (atomic + molecular) in the gas clouds surrounding the young star cluster. A graph in logarithmic coordinates of the cluster UV luminosity versus the total density in the surrounding gas provides a direct measure of the exponent of the star formation law. However, we show that this plot is severely affected by observational selection, which renders large areas of the diagram inaccessible to the data. An ordinary least-squares regression fit to a straight line, therefore, gives a strongly biased result. In the present case, the slope of such a fit primarily reflects the boundary defined when the 21 cm line becomes optically thick and is no longer a reliable measure of the H i column density. We use a maximum likelihood statistical approach which can deal with truncated and skewed data, and also takes account of the large uncertainties in the total gas densities which we derive. The exponent we obtain for the Schmidt law in M33 is 1.4 ± 0.2.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".