Systematic uncertainties in models of the cosmic dawn
Bibliographic record
Abstract
ABSTRACT Models of the reionization and reheating of the intergalactic medium (IGM) at redshifts z ≳ 6 continue to grow more sophisticated in anticipation of near-future 21-cm, cosmic microwave background, and galaxy survey measurements. However, there are many potential sources of systematic uncertainty in models that could bias and/or degrade upcoming constraints if left unaccounted for. In this work, we examine three commonly ignored sources of uncertainty in models for the mean reionization and thermal histories of the IGM: the underlying cosmology, halo mass function (HMF), and choice of stellar population synthesis (SPS) model. We find that cosmological uncertainties affect the Thomson scattering optical depth at the few per cent level and the amplitude of the global 21-cm signal at the ∼5–10 mK level. The differences brought about by choice of HMF and SPS models are more dramatic, comparable to the 1σ error bar on τe and an ∼20 mK effect on the global 21-cm signal amplitude. Finally, we jointly fit galaxy luminosity functions and global 21-cm signals for all HMF/SPS combinations and find that (i) doing so requires additional free parameters to compensate for modelling systematics and (ii) the spread in constraints on parameters of interest for different HMF and SPS choices, assuming 5 mK noise in the global signal, is comparable to those obtained when adopting the 'true' HMF and SPS with ≳20 mK errors. Our work highlights the need for dedicated efforts to reduce modelling uncertainties in order to enable precision inference with future data sets.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".