The <scp>thesan-zoom</scp> project: star formation efficiency from giant molecular clouds to galactic scale in high-redshift starbursts
Bibliographic record
Abstract
ABSTRACT Star formation in galaxies is inherently complex, involving the interplay of physical processes over a hierarchy of spatial scales. In this work, we investigate the connection between global (galaxy-scale) and local (cloud-scale) star formation efficiencies (SFEs) at high redshifts ($z\gtrsim 3$), using the state-of-the-art cosmological zoom-in simulation suite thesan-zoom. We find that the galaxy-scale average SFE, $\langle \epsilon ^{\rm gal}_{\rm ff} \rangle$, scales with $M_{\rm halo}^{1/3}\, (1+z)^{1/2} \sim V_{\rm vir}$, consistent with expectations from feedback-regulated models. On cloud scales, we identify giant molecular clouds (GMCs) in a broad sample of high-redshift starbursts spanning a wide range of halo masses and redshifts. Star formation in these systems is predominantly hosted by filamentary GMCs embedded in a dense and highly turbulent interstellar medium (ISM). GMCs exhibit remarkably universal properties, including mass function, size, turbulence, and surface density, regardless of the environment in which they are identified. The global gas depletion time (and the Kennicutt–Schmidt relation) is determined by the GMC mass fraction in the ISM, while the cloud-scale SFE shows little variation. In particular, we find a nearly constant gas surface density of $\Sigma _{\rm GMC} \approx 70 {\, \rm M_\odot }\, \mathrm{pc}^{-2}$ across different host galaxies. Nevertheless, we identify two regimes where phases with high SFE can arise. First, stars may form efficiently in the shock fronts generated by feedback from a preceding starburst. Secondly, the increasing background dark matter surface density with redshift may contribute to the gravitational potential of clouds at $z \gtrsim 8$ and confine them in high-SFE phases over extended periods.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".