Bridging the Gap between Cosmic Dawn and Reionization Favors Models Dominated by Faint Galaxies
Bibliographic record
Abstract
Abstract It has been claimed that traditional models struggle to explain the tentative detection of the 21 cm absorption trough centered at z ∼ 17 measured by the EDGES collaboration. On the other hand, it has been shown that the EDGES results are consistent with an extrapolation of a declining UV luminosity density, following a simple power law of deep Hubble Space Telescope observations of 4 < z < 9 galaxies. We here explore the conditions by which the EDGES detection is consistent with current reionization and post-reionization observations, including the neutral hydrogen fraction at z ∼ 6–8, Thomson-scattering optical depth, and ionizing emissivity at z ∼ 5. By coupling a physically motivated source model derived from radiative transfer hydrodynamic simulations of reionization to a Markov Chain Monte Carlo sampler, we find that it is entirely possible to reconcile existing high-redshift (cosmic dawn) and low-redshift (reionization) constraints. In particular, we find that high contributions from low-mass halos along with high photon escape fractions are required to simultaneously reproduce cosmic dawn and reionization constraints. Our analysis further confirms that low-mass galaxies produce a flatter emissivity evolution, which leads to an earlier onset of reionization with a gradual and longer duration, resulting in a higher optical depth. While the models dominated by faint galaxies successfully reproduce the measured globally averaged quantities over the first one billion years, they underestimate the late redshift-instantaneous measurements in efficiently star-forming and massive systems. We show that our (simple) physically motivated semianalytical prescription produces results that are consistent with the (sophisticated) state-of-the-art THESAN radiation-magnetohydrodynamic simulation of the reionization.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".