Gas accretion at high redshift: cold flows all the way
Bibliographic record
Abstract
We study in detail how massive galaxies accrete gas through cosmic time using cosmological hydrodynamical simulations from the High-z Evolution of Large and Luminous Objects (HELLO) and the Numerical Investigation of a Hundred Astrophysical Objects (NIHAO) projects. We find that accretion through cold filaments at high redshift (z ~ 2-4) is a key factor in maintaining the high star formation rates (> 100 Msun/yr) observed in these galaxies, and that more than 75% of the total gas participating in the star formation process is accreted via this channel at high z even in haloes well above 10^12 Msun. The low volume occupancy of the filaments allows plenty of space for massive gas outflows generated by the vigorous star formation and AGN activity, with the cold incoming gas and the hot outflowing gas barely interacting. We present a model based on a Bayesian hierarchical formalism that accurately describes the evolution of the cold fraction accretion with redshift and halo mass. Our model predicts a relatively constant critical mass (Mc) for the cold-to-hot transition up to z ~ 1.3 and an evolving critical mass log(Mc) proportional to log(1+z)^1.7 at higher redshift. Overall, our findings provide deeper insight into the cosmic evolution of gas accretion modes and offer a robust framework for understanding how cold accretion contributes to galaxy growth across different epochs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; both teacher heads agree on what is shown here.
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".