3D intrinsic shapes of quiescent galaxies in observations and simulations
Bibliographic record
Abstract
ABSTRACT We study the intrinsic 3D shapes of quiescent galaxies over the last half of cosmic history based on their axial ratio distribution. To this end, we construct a sample of unprecedented size, exploiting multiwavelength u-to-Ks photometry from the deep wide-area surveys KiDS+VIKING paired with high-quality i-band imaging from HSC-SSP. The dependences of the shapes on mass, redshift, photometric bulge prominence and environment are considered. For comparison, the intrinsic shapes of quenched galaxies in the IllustrisTNG simulations are analysed and contrasted with their formation history. We find that over the full 0 < z < 0.9 range, and in both simulations and observations, spheroidal 3D shapes become more abundant at $M_* \gt 10^{11}\, \mathrm{M}_{\odot }$, with the effect being most pronounced at lower redshifts. In TNG, the most massive galaxies feature the highest ex situ stellar mass fractions, pointing to violent relaxation via mergers as the mechanism responsible for their 3D shape transformation. Larger differences between observed and simulated shapes are found at low to intermediate masses. At any mass, the most spheroidal quiescent galaxies in TNG feature the highest bulge mass fractions, and, conversely, observed quiescent galaxies with the highest bulge-to-total ratios are found to be intrinsically the roundest. Finally, we detect an environmental influence on galaxy shape, at least at the highest masses, such that at fixed mass and redshift, quiescent galaxies tend to be rounder in denser environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".