The <scp>thesan</scp> project: Lyman-α emitter luminosity function calibration
Bibliographic record
Abstract
ABSTRACT The observability of Lyα emitting galaxies (LAEs) during the Epoch of Reionization can provide a sensitive probe of the evolving neutral hydrogen gas distribution, thus setting valuable constraints to distinguish different reionization models. In this study, we utilize the new thesan suite of large-volume ($L_\text{box} = 95.5\, \text{cMpc}$) cosmological radiation-hydrodynamic simulations to directly model the Lyα emission from individual galaxies and the subsequent transmission through the intergalactic medium. thesan combines the arepo-rt radiation-hydrodynamic solver with the IllustrisTNG galaxy formation model and includes high- and medium-resolution simulations designed to investigate the impacts of halo-mass-dependent escape fractions, alternative dark matter models, and numerical convergence. We find important differences in the Lyα transmission based on reionization history, bubble morphology, frequency offset from line centre, and galaxy brightness. For a given global neutral fraction, Lyα transmission reduces when low-mass haloes dominate reionization over high-mass haloes. Furthermore, the variation across sightlines for a single galaxy is greater than the variation across all galaxies. This collectively affects the visibility of LAEs, directly impacting observed Lyα luminosity functions (LFs). We employ Gaussian Process Regression using SWIFTEmulator to rapidly constrain an empirical model for dust escape fractions and emergent spectral-line profiles to match observed LFs. We find that dust strongly impacts the Lyα transmission and covering fractions of MUV ≲ −19 galaxies in $M_\text{vir} \gtrsim 10^{11}\, \text{M}_{\bigodot }$ haloes, such that the dominant mode of removing Lyα photons in non-LAEs changes from low-IGM transmission to high dust absorption around z ∼ 7.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".