Fitting the Shadows: Star Formation Scaling Relations in the Low Surface Brightness Regime
Bibliographic record
Abstract
Abstract Classical low surface brightness (LSB) galaxies pose an important challenge to galaxy evolution models. While they are found to host large reservoirs of atomic hydrogen, they display low stellar and star formation surface densities. Global star formation scaling relations characterize trends in the star formation behaviour of galaxies; when used to compare populations or classes of galaxies, deviations in the observed trends can be used to probe predicted differences in physical conditions. In this work, we utilize the well-studied Star-forming Main Sequence and integrated Kennicutt–Schmidt Relations to characterize star formation in the LSB regime, and compare the observed trends to relations for normal star-forming galaxies. Using a comprehensive cross-matched sample of 277 LSB galaxies from the GALEX-SDSS-WISE Legacy Catalog Release 2 and the Arecibo Legacy Fast Arecibo L-band Feed Array Catalog, we gain an in-depth view of the star formation process in the LSB regime. H i-selected LSB galaxies follow very similar trends in atomic gas-to-stellar mass ratio and the star-forming main sequence to their high surface brightness counterparts. However, while LSB galaxies host comparably large atomic gas reservoirs, they prove to be largely inefficient in converting this gas to stars with a median depletion time t dep = ∼18 Gyr. These results are discussed in relation to previous studies, which find that LSB galaxies host low atomic gas densities and are largely deficient in molecular gas, which suggests that the faint appearance of LSB galaxies may be the result of physical conditions on the sub-kpc scale.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".