The <scp>thesan</scp> project: tracking the expansion and merger histories of ionized bubbles during the Epoch of Reionization
Bibliographic record
Abstract
ABSTRACT The growth of ionized hydrogen bubbles in the intergalactic medium around early luminous objects is a fundamental process during the Epoch of Reionization (EoR). Observations using Ly $\alpha$ emission from high-redshift galaxies and forthcoming 21 cm maps are beginning to constrain the sizes of these ionized regions. In this study, we analyse bubble sizes and their evolution using the state-of-the-art thesan radiation-hydrodynamics simulation suite, which self-consistently models radiation transport and realistic galaxy formation throughout a large $(95.5\, \text{cMpc})^3$ volume of the universe. Analogous to the accretion and merger tree histories employed in galaxy formation simulations, we characterize the growth and merger rates of ionized bubbles by focusing on the spatially resolved redshift of reionization. By tracing the chronological expansion of bubbles, we partition the simulation volume and construct a natural ionization history. We identify three distinct stages of ionized growth: (1) initial slow expansion around the earliest ionizing sources, (2) accelerated growth through percolation, and (3) rapid expansion dominated by the largest bubble. Notably, we find that the largest bubble emerges by $z \approx 9\!-\!10$, well before the midpoint of reionization. This bubble becomes dominant during the second growth stage, and defines the third stage by rapidly expanding to encompass the remainder of the simulation volume. Additionally, we observe a sharp decline in the number of bubbles with radii around $\sim 10$ cMpc, indicating a characteristic scale in the final segmented size distribution. Overall, these chronologically sequenced spatial reconstructions offer crucial insights into the physical mechanisms driving ionized bubble growth during the EoR, providing a framework for interpreting reionization itself.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".