The structural and size evolution of star-forming galaxies over the last 11 Gyrs
Bibliographic record
Abstract
We present new results on the evolution of rest-frame blue/UV sizes and Sersic indices of H$α$-selected star-forming galaxies over the last 11 Gyrs. We investigate how the perceived evolution can be affected by a range of biases and systematics such as cosmological dimming and resolution effects. We use GALFIT and an artificial redshifting technique, which includes the luminosity evolution of H$α$-selected galaxies, to quantify the change on the measured structural parameters with redshift. We find typical sizes of 2 to 3 kpc and Sersic indices of n~1.2, close to pure exponential disks all the way from z=2.23 to z=0.4. At z=0 we find typical sizes of 4-5 kpc. Our results show that, when using GALFIT, cosmological dimming has a negligible impact on the derived effective radius for galaxies with <10 kpc, but we find a ~20% bias on the estimate of the median Sersic indices, rendering galaxies more disk-like. Star-forming galaxies have grown on average by a factor of 2-3 in the last 11 Gyrs with $r_e\propto(1+z)^{-0.75}$. By exploring the evolution of the stellar mass-size relation we find evidence for a stronger size evolution of the most massive star-forming galaxies since z~2, as they grow faster towards z~0 when compared to the lower stellar mass counterparts. As we are tracing the rest-frame blue/UV, we are likely witnessing the growth of disks where star formation is ongoing in galaxies while their profiles remain close to exponential disks, n<1.5, across the same period.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".