WITNESSING THE DIFFERENTIAL EVOLUTION OF DISK GALAXIES IN LUMINOSITY AND SIZE VIA GRAVITATIONAL LENSING
Bibliographic record
Abstract
We take advantage of the magnification in size and flux of a galaxy provided by gravitational lensing to analyze the properties of 62 strongly lensed galaxies from the Sloan Lens ACS (SLACS) Survey. The sample of lensed galaxies spans a redshift range of 0.20 ⩽ z ⩽ 1.20 with a median redshift of z = 0.61. We use the lens modeling code LENSFIT to derive the luminosities, sizes, and Sérsic indices of the lensed galaxies. The measured properties of the lensed galaxies show a primarily compact, "disk"-like population with the peaks of the size and Sérsic index distributions corresponding to ∼1.50 kpc and n ∼ 1, respectively. Comparison of the SLACS galaxies to a non-lensing, broadband imaging survey shows that a lensing survey allows us to probe a galaxy population that reaches ∼2 mag fainter. Our analysis allows us to compare the 〈 z 〉 = 0.61 disk galaxy sample ( n ⩽ 2.5) to an unprecedented local galaxy sample of ∼670, 000 SDSS galaxies at z ∼ 0.1; this analysis indicates that the evolution of the luminosity–size relation since z ∼ 1 may not be fully explained by a pure-size or pure-luminosity evolution but may instead require a combination of both. Our observations are also in agreement with recent numerical simulations of disk galaxies that show evidence of a mass-dependent evolution since z ∼ 1, where high-mass disk galaxies ( M ⋆ > 10 9 M ☉ ) evolve more in size and low-mass disk galaxies ( M ⋆ ⩽ 10 9 M ☉ ) evolve more in luminosity.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".