On the spin-temperature evolution during the epoch of reionization
Bibliographic record
Abstract
Simulations estimating the brightness temperature (δTb) of the redshifted 21 cm from the epoch of reionization (EoR) often assume that the spin temperature (Ts) is decoupled from the background cosmic microwave background (CMB) temperature and is much larger than it, i.e. Ts≫TCMB. Although a valid assumption towards the later stages of the reionization process, it does not necessarily hold at the earlier epochs. Violation of this assumption will lead to fluctuations in δTb that are driven neither by density fluctuations nor by H ii regions. Therefore, it is vital to calculate the spin temperature self-consistently by treating the Lyα and collisional coupling of Ts to the kinetic temperature, Tk. In this paper we develop an extension to the bears algorithm, originally developed to model reionization history, to include these coupling effects. Here, we simulate the effect in ionization and heating for three models in which the reionization is driven by stars, mini-QSOs or a mixture of both. We also perform a number of statistical tests to quantify the imprint of the self-consistent inclusion of the spin-temperature decoupling from the CMB. We find that the evolution of the spin temperature has an impact on the measured signal especially at redshifts higher than 10 and such evolution should be taken into account when one attempts to interpret the observational data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".