Gas Accretion and Mergers in Massive Galaxies at <i>z</i> ~ 2
Bibliographic record
Abstract
Abstract Galaxy assembly is an unsolved problem, with ΛCDM theoretical models unable to easily account for among other things, the abundances of massive galaxies, and the observed merger history. We show here how the problem of galaxy formation can be addressed in an empirical way without recourse to models. We discuss how galaxy assembly occurs at 1.5 < z < 3 examining the role of major and minor mergers, and gas accretion from the intergalactic medium in forming massive galaxies with log M* > 11 found within the GOODS NICMOS Survey (GNS). We find that major mergers, minor mergers and gas accretion are roughly equally important in the galaxy formation process during this epoch, with 64% of the mass assembled through merging and 36% through accreted gas which is later converted to stars, while 58% of all new star formation during this epoch arises from gas accretion. We also discuss how the total gas accretion rate is measured as Ṁ = 90±40 M⊙ yr−1 at this epoch, a value close to those found in some hydrodynamical simulations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".