The Dawn of Disk Formation in a Milky Way-sized Galaxy Halo: Thin Stellar Disks at z > 4
Bibliographic record
Abstract
Abstract We present results from GigaEris , a cosmological, N -body hydrodynamical “zoom-in” simulation of the formation of a Milky Way-sized galaxy halo with unprecedented resolution, encompassing of order a billion particles within the refined region. The simulation employs a modern implementation of smoothed-particle hydrodynamics, including metal-line cooling and metal and thermal diffusion. We focus on the early assembly of the galaxy, down to redshift z = 4.4. The simulated galaxy has properties consistent with extrapolations of the main sequence of star-forming galaxies to higher redshifts and levels off to a star formation rate of ∼60 M ⊙ yr −1 at z = 4.4. A compact, thin rotating stellar disk with properties analogous to those of low-redshift systems arises already at z ∼ 8. The galaxy rapidly develops a multi-component structure, and the disk, at least at these early stages, does not grow “upside-down” as often reported in the literature. Rather, at any given time, newly born stars contribute to sustain a thin disk. The kinematics reflect the early, ubiquitous presence of a thin disk, as a stellar disk component with v ϕ / σ R larger than unity is already present at z ∼ 9–10. Our results suggest that high-resolution spectro-photometric observations of very high-redshift galaxies should find thin rotating disks, consistent with the recent discovery of cold rotating gas disks by ALMA. Finally, we present synthetic images for the James Webb Space Telescope NIRCam camera, showing how the early disk would be easily detectable already at those early times.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".