The <scp>thesan</scp> project: connecting ionized bubble sizes to their local environments during the Epoch of Reionization
Bibliographic record
Abstract
ABSTRACT An important characteristic of cosmic hydrogen reionization is the growth of ionized gas bubbles surrounding early luminous objects. Ionized bubble sizes are beginning to be probed using Lyman α emission from high-redshift galaxies, and will also be probed by upcoming 21 cm maps. We present results from a study of bubble sizes using the state-of-the-art thesan radiation-hydrodynamics simulation suite, which self-consistently models radiation transport and realistic galaxy formation. We employ the mean free path method and track the evolution of the effective ionized bubble size at each point (Reff) throughout the Epoch of Reionization. We show that there is a slow growth period for regions ionized early, but a rapid ‘flash ionization’ process for regions ionized later as they immediately enter a large, pre-existing bubble. We also find that bright sources are preferentially in larger bubbles, and find consistency with recent observational constraints at z ≳ 9, but tension with idealized Lyman α damping-wing models at z ≈ 7. We find that high-overdensity regions have larger characteristic bubble sizes, but the correlation decreases as reionization progresses, likely due to runaway formation of large percolated bubbles. Finally, we compare the redshift at which a region transitions from neutral to ionized (zreion) with the time it takes to reach a given bubble size and conclude that zreion is a reasonable local probe of small-scale bubble size statistics ($R_\text{eff} \lesssim 1\, \rm {cMpc}$). However, for larger bubbles, the correspondence between zreion and size statistics weakens due to the time delay between the onset of reionization and the expansion of large bubbles, particularly at high redshifts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".