THE LINK BETWEEN LIGHT AND MASS IN LATE-TYPE SPIRAL GALAXY DISKS
Bibliographic record
Abstract
We present the correlation between the extrapolated central disk surface brightness (μ) and extrapolated central surface mass density (Σ) for galaxies in the DiskMass sample. This μ–Σ relation has a small scatter of 30% at the high surface brightness (HSB) end. At the low surface brightness (LSB) end, galaxies fall above the μ–Σ relation, which we attribute to their higher dark matter content. After correcting for the dark matter as well as for the contribution of gas and the effects of radial gradients in the disk, the LSB end falls back on the linear μ–Σ relation. The resulting scatter around the corrected μ–Σ relation is 25% at the HSB end and about 50% at the LSB end. The intrinsic scatter in the μ–Σ relation is estimated to be 10%–20%. Thus, if μ K , 0 is known, the stellar surface mass density is known to within 10%–20% (random error). Assuming disks have an exponential vertical distribution of mass, the average is 0.24 M ☉ / L ☉ , with an intrinsic scatter around the mean of at most 0.05 M ☉ / L ☉ . This value for is 20% smaller than we found in Martinsson et al., mainly due to the correction for dark matter applied here. This small scatter means that among the galaxies in our sample, variations in scale height, vertical density profile shape, and/or the ratio of vertical over radial velocity dispersion must be small.
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| 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".