The galaxy-IGM connection in THESAN: the physics connecting the IGM Lyman- <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:mi>α</a:mi> </a:math> opacity and galaxy density in the reionization epoch
Bibliographic record
Abstract
The relation between the Lyman- α effective optical depth of quasar sightlines ( τ l o s ) and the distribution of galaxies around them is an emerging probe of the connection between the first collapsed structures and the IGM properties at the tail end of cosmic reionization. We employ the THESAN simulations to demonstrate that τ l o s is most sensitive to galaxies at a redshift-dependent distance, reflecting the growth of ionized regions around sources of photons and in agreement with studies of the galaxy–Lyman- α cross correlation. This is d ∼ 15 h − 1 M p c at the tail end of reionization. The flagship THESAN run struggles to reproduce the most opaque sightlines as well as those with large galaxy densities, likely as a consequence of its limited volume. We identify a promising region of parameter space to probe with future observations in order to distinguish both the timing and sources of reionization. We present an investigation of the IGM physical conditions around opaque and transparent spectra, revealing that they probe regions that reionized inside-out and outside-in, respectively, and demonstrate that, for the range of optical depths probed by our simulation, residual neutral islands at the end of reionization are not required to produce highly opaque sightlines although they facilitate the task. Finally, we investigate the sensitivity of the aforementioned results to the nature of ionizing sources and dark matter.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".