The <scp>thesan–zoom</scp> project: long-term imprints of external reionization on galaxy evolution
Bibliographic record
Abstract
ABSTRACT We investigate the impact of ionizing external ultraviolet (UV) radiation on low-mass haloes ($M_\text{halo}\lesssim 10^{10}\, {\rm M_\odot }$) at high redshift ($z\ge 3$) using $1140\, {\rm M_\odot }$ baryonic resolution zoom-in simulations of seven thesan–zoom regions. We compare three simulation sets that differ in the treatment of external UV radiation: one employing a uniform UV background initiated at $z=10.6$ in addition to radiation transport for local sources, another with the background starting at $z=5.5$, and the default configuration using the large-scale radiation from the parent thesan-1 simulation as a boundary condition. The multiphase interstellar medium (ISM) model, combined with its high mass resolution, allows us to resolve all star-forming haloes and capture the back-reaction of ionizing radiation on galaxy properties during the epoch of reionization. When present, external UV radiation efficiently unbinds gas in haloes with masses below $10^9{\, \rm M_\odot }$ and suppresses subsequent star formation. As a result, in simulations with early reionization, minihaloes fail to form stars from pristine gas, leading to reduced metal enrichment of gas later accreted by more massive haloes. Consequently, haloes with masses below $10^{10}{\, \rm M_\odot }$ at all simulated epochs ($z>3$) exhibit lower metallicities and altered metallicity distributions. The more accurate and realistic shielding from external UV radiation, achieved through self-consistent radiative transfer, permits the existence of a cold but low-density gas phase down to $z=3$. These findings highlight the importance of capturing a patchy reionization in high-resolution simulations targeting high-redshift galaxy formation. We conclude that at minimum, semi-numerical models that incorporate spatially inhomogeneous reionization and a non-uniform metallicity floor are necessary to accurately emulate metal enrichment in minihaloes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".