THE ROLE OF COLD FLOWS IN THE ASSEMBLY OF GALAXY DISKS
Bibliographic record
Abstract
We use high-resolution cosmological hydrodynamical simulations to demonstrate that cold flow gas accretion, particularly along filaments, modifies the standard picture of gas accretion and cooling onto galaxy disks. In the standard picture, all gas is initially heated to the virial temperature of the galaxy as it enters the virial radius. Low-mass galaxies are instead dominated by accretion of gas that stays well below the virial temperature, and even when a hot halo is able to develop in more massive galaxies there exist dense filaments that penetrate inside of the virial radius and deliver cold gas to the central galaxy. For galaxies up to ∼ L *, this cold accretion gas is responsible for the star formation (SF) in the disk at all times to the present. Even for galaxies at higher masses, cold flows dominate the growth of the disk at early times. Within this modified picture, galaxies are able to accrete a large mass of cold gas, with lower initial gas temperatures leading to shorter cooling times to reach the disk. Although SF in the disk is mitigated by supernovae feedback, the short cooling times allow for the growth of stellar disks at higher redshifts than predicted by the standard model.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".