Size–Luminosity Scaling Relations of Local and Distant Star-forming Regions
Bibliographic record
Abstract
Abstract We investigate star-forming scaling relations using Bayesian inference on a comprehensive data sample of low- ( z < 0.1) and high-redshift (1 < z < 5) star-forming regions. This full data set spans a wide range of host galaxy stellar mass ( M * ∼ 10 6 –10 11 ) and clump star formation rates (SFR ∼ 10 −5 −10 2 yr −1 ). We fit the power-law relationship between the size ( ) and luminosity ( ) of the star-forming clumps using the Bayesian statistical modeling tool Stan, which makes use of Markov Chain Monte Carlo (MCMC) sampling techniques. Trends in the scaling relationship are explored for the full sample and subsets based on redshift and selection effects between samples. In our investigation, we find neither evidence of redshift evolution of the size–luminosity scaling relationship nor a difference in slope between lensed and unlensed data. There is evidence of a break in the scaling relationship between high and low SFR surface density ( ) clumps. The size–luminosity power-law fit results are ∼ 2.8 and ∼ 1.7 for low and high clumps, respectively. We present a model where star-forming clumps form at locations of gravitational instability and produce an ionized region represented by the Strömgren radius. A radius smaller than the scale height of the disk results in a scaling relationship of L ∝ r 3 (high clumps), and a scaling of L ∝ r 2 (low clumps) if the radius is larger than the disk scale height.
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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 | 0.003 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".