High-redshift Massive Quiescent Galaxies Are as Flat as Star-forming Galaxies: The Flattening of Galaxies and the Correlation with Structural Properties in CANDELS/3D-HST
Bibliographic record
Abstract
Abstract We investigate the median flattening of galaxies at 0.2 < z < 4.0 in all five CANDELS/3D-HST fields via the apparent axis ratio, q. We separate the sample into bins of the redshift, stellar mass, Sérsic index, size, and UVJ-determined star-forming state to discover the most important drivers of the median q (q med). Quiescent galaxies at z < 1 and M * > 1011 M ⊙ are rounder than those at lower masses, consistent with the hypothesis that they have grown significantly through dry merging. The massive quiescent galaxies at higher redshifts become flatter and are as flat as star-forming massive galaxies at 2.5 < z < 3.5, consistent with formation through direct transformations or wet mergers. We find that in quiescent galaxies, correlations with q med and M *, and z and r e are driven by the evolution in the Sérsic index (n), consistent with the growing accumulation of minor mergers at lower redshifts. Interestingly, n does not drive these trends fully in star-forming galaxies. Instead, the strongest predictor of q in star-forming galaxies is the effective radius, where larger galaxies are flatter. Our findings suggest that q med is the tracing bulge-to-total ratio, which would explain why smaller/more massive star-forming galaxies are rounder than their extended/less massive analogs, although it is unclear why the Sérsic index correlates more weakly with flattening for star-forming galaxies than for quiescent 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".