BREAKS IN THIN AND THICK DISKS OF EDGE-ON GALAXIES IMAGED IN THE<i>SPITZER</i>SURVEY OF STELLAR STRUCTURE IN GALAXIES (S<sup>4</sup>G)
Bibliographic record
Abstract
Breaks in the radial luminosity profiles of galaxies have until now been mostly studied averaged over disks. Here, we study separately breaks in thin and thick disks in 70 edge-on galaxies using imaging from the Spitzer Survey of Stellar Structure in Galaxies. We built luminosity profiles of the thin and thick disks parallel to midplanes and we found that thin disks often truncate (77%). Thick disks truncate less often (31%), but when they do, their break radius is comparable with that in the thin disk. This suggests either two different truncation mechanisms—one of dynamical origin affecting both disks simultaneously and another one only affecting the thin disk—or a single mechanism that creates a truncation in one disk or in both depending on some galaxy property. Thin disks apparently antitruncate in around 40% of galaxies. However, in many cases, these antitruncations are an artifact caused by the superposition of a thin disk and a thick disk, with the latter having a longer scale length. We estimate the real thin disk antitruncation fraction to be less than 15%. We found that the ratio of the thick and thin stellar disk mass is roughly constant (0.2 < M T / M t < 0.7) for circular velocities v c > 120 km s −1 , but becomes much larger at smaller velocities. We hypothesize that this is due to a combination of a high efficiency of supernova feedback and a slower dynamical evolution in lower-mass galaxies causing stellar thin disks to be younger and less massive than in higher-mass galaxies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".