The Number Densities and Stellar Populations of Massive Galaxies at 3 < z < 6: A Diverse, Rapidly Forming Population in the Early Universe
Bibliographic record
Abstract
Abstract We present the census of massive (log(M */M ⊙) > 11) galaxies at 3 < z < 6 identified over the COSMOS/UltraVISTA Ultra-Deep field stripes: consisting of ≈100 and ≈20 high-confidence candidates at 3 < z < 4 and 4 < z < 6, respectively. The 3 < z < 4 population is comprised of post-starburst, UV-star-forming, and dusty star-forming galaxies in roughly equal fractions, while UV-star-forming galaxies dominate at 4 < z < 6 . We account for various sources of biases in the spectral energy distribution (SED) modeling, finding that the treatment of emission line contamination is essential for understanding the number densities and mass growth histories of massive galaxies at z > 3. The significant increase in observed number densities at z ∼ 4 (> × 5 in ≲600 Myr) implies that this is the epoch at which log(M */M ⊙) > 11 galaxies emerge in significant numbers, with stellar ages (≈500–900 Myr) indicating rapid formation epochs as early as z ∼ 7. Leveraging ancillary multiwavelength data sets, we perform panchromatic SED modeling to constrain the total star formation activity of the sample. The star formation activity of the sample is generally consistent with being on the star formation main sequence at the considered redshifts, with ≈15%–25% of the population showing evidence of suppressed star formation rates, indicating that quenching mechanisms are already at play by z ∼ 4. We stack the available Hubble Space Telescope imaging, confirming their compact nature (r e ≲ 2.2 kpc), consistent with expected sizes of high-z star-forming galaxies. Finally, we discuss how our results are in-line with the early formation epochs and short formation timescales inferred from the fossil records of the most massive galaxies in the universe.
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 | 0.000 |
| 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".