The Size Function of Galaxy Disks out to<i>z</i>∼ 1 from the Canada‐France‐Hawaii‐Telescope Legacy Survey
Bibliographic record
Abstract
The formation and growth of galaxy disks over cosmic time is crucial to our understanding of galaxy formation. Despite steady improvements in the size and quality of disk samples over the last decade, many aspects of galaxy disk evolution remain unclear. Using 2 deg 2 of deep, wide-field i '-band imaging from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS), we compute size functions for 6000 disks from z = 0.2 to z = 1 and explore luminosity and number density evolution scenarios with an emphasis on the importance of selection effects on the interpretation of the data. We also compute the size function of a very large sample of disks from the Sloan Digital Sky Survey to use as a local ( z ≃ 0.1) comparison. CFHTLS size functions computed with the same fixed luminosity-size selection window at all redshifts exhibit evolution that appears to be best modeled by a pure number density evolution. The z = 0.3 size function is an excellent match to the z = 0.9 one if disks at the highest redshift are a factor of 2.5 more abundant than in the local universe. The SDSS size function would also match the z = 0.9 CFHTLS size function very well with a similar change in number density. On the other hand, the CFHTLS size functions computed with a varying luminosity-size selection window with redshift remain constant if the selection window is shifted by 1.0-1.5 mag toward fainter magnitudes with decreasing redshift. There is a weak dependence on disk scale length with smaller ( h ≲ 4 kpc) disks requiring more luminosity evolution than larger ones. Given that changes in number density are primarily due to mergers and that current estimates of merger rates below z = 1 are low, luminosity evolution appears to be a more plausible scenario to explain the observations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".