Quiescent Galaxy Size and Spectroscopic Evolution: Combining HSC Imaging and Hectospec Spectroscopy
Bibliographic record
Abstract
Abstract We explore the relationships between size, stellar mass, and average stellar population age (indicated by D n 4000 indices) for a sample of ∼11,000 intermediate-redshift galaxies from the SHELS spectroscopic survey (Geller et al. 2014) augmented by high-resolution Subaru Telescope Hyper Suprime-Cam imaging. In the redshift interval 0.1 < z < 0.6, star-forming galaxies are on average larger than their quiescent counterparts. The mass-complete sample of ∼3500 quiescent galaxies shows that the average size of a quiescent galaxy increases by ≲25% from z ∼ 0.6 to z ∼ 0.1. This growth rate is a function of stellar mass: the most massive ( ) galaxies grow significantly more slowly in size than quiescent systems an order of magnitude less massive that grow by 70% in the 0.1 ≲ z ≲ 0.3 redshift interval. For galaxies, age and size are anticorrelated at fixed mass; more massive quiescent systems show no significant trend in size with average stellar population age. The evolution in absolute and fractional abundances of quiescent systems at intermediate redshift are also a function of galaxy stellar mass. The suite of evolutionary trends suggests that galaxies more massive than have mostly assembled their mass by z ∼ 0.6. Quiescent galaxies with lower stellar masses show more complex evolution that is characterized by a combination of individual quiescent galaxy size growth (through mergers) and an increase in the size of newly quenched galaxies joining the population at later times (progenitor bias). The low-mass population ( ) grows predominantly as a result of progenitor bias. For more massive ( ) quiescent galaxies, (predominantly minor) mergers and progenitor bias make more comparable contributions to the size growth. At intermediate redshift, quiescent size growth is mass-dependent; the most massive ( ) galaxies experience the least rapid increase in size from z ∼ 0.6 to z ∼ 0.1.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".