EVOLUTION OF GALAXIES AND THEIR ENVIRONMENTS AT <i>z</i> = 0.1-3 IN COSMOS
Bibliographic record
Abstract
Large-scale structures (LSSs) out to z < 3.0 are measured in the Cosmic Evolution Survey (COSMOS) using extremely accurate photometric redshifts (photoz). The K s -band-selected sample (from Ultra-Vista) is comprised of 155,954 galaxies. Two techniques—adaptive smoothing and Voronoi tessellation—are used to estimate the environmental densities within 127 redshift slices. Approximately 250 statistically significant overdense structures are identified out to z = 3.0 with shapes varying from elongated filamentary structures to more circularly symmetric concentrations. We also compare the densities derived for COSMOS with those based on semi-analytic predictions for a ΛCDM simulation and find excellent overall agreement between the mean densities as a function of redshift and the range of densities. The galaxy properties (stellar mass, spectral energy distributions (SEDs), and star formation rates (SFRs)) are strongly correlated with environmental density and redshift, particularly at z < 1.0–1.2. Classifying the spectral type of each galaxy using the rest-frame b − i color (from the photoz SED fitting), we find a strong correlation of early-type galaxies (E-Sa) with high-density environments, while the degree of environmental segregation varies systematically with redshift out to z ∼ 1.3. In the highest density regions, 80% of the galaxies are early types at z = 0.2 compared to only 20% at z = 1.5. The SFRs and the star formation timescales exhibit clear environmental correlations. At z > 0.8, the SFR density is uniformly distributed over all environmental density percentiles, while at lower redshifts the dominant contribution is shifted to galaxies in lower density environments.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".