A systematic search for galaxy protocluster cores at the transition epoch of their star formation activity
Bibliographic record
Abstract
ABSTRACT The redshift of z ∼ 1.5 is the transition epoch of protoclusters (PCs) from the star-forming phase into the quenching phase, and hence an appropriate era to investigate the build up of the quenched population. We define a ‘core’ as the most massive halo in a given PC, where environmental effects are likely to work most effectively, and search for cores at 1 < z < 1.5. We use a photometric redshift catalogue of a wide (effective area of ${\sim}22.2\, \mathrm{deg}^{2}$) and deep ($i\sim 26.8\, \mathrm{mag}$) optical survey with Subaru Hyper-Suprime Cam. Regarding galaxies with log (M*/M⊙) > 11.3 as the central galaxies of PC cores, we estimate their average halo mass by clustering analysis and find it to be log (Mh/M⊙) ∼ 13.7. An expected mass growth by the IllustrisTNG simulation and the observed overdensities around them suggest that the PC cores we find are progenitors of present-day clusters. Classifying our galaxy sample into red and blue galaxies, we calculate the stellar mass function (SMF) and the red galaxy fraction. The SMFs in the PC cores are more-top heavy than field, implying early high-mass galaxy formation and disruption of low-mass galaxies. We also find that the red fraction increases with stellar mass, consistent with stellar mass dependent environmental quenching recently found at z > 1. Interestingly, although the cores with red and blue centrals have similar halo masses, only those with red centrals show a significant red fraction excess compared to the field, suggesting a conformity effect. Some observational features of PC cores may imply that the conformity is caused by assembly bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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".