Beyond ΛCDM with H i intensity mapping: robustness of cosmological constraints in the presence of astrophysics
Bibliographic record
Abstract
ABSTRACT Mapping the unresolved intensity of the 21-cm emission of neutral hydrogen (H i) is now regarded as one the most promising tools for cosmological investigation in the coming decades. Here, we investigate, for the first time, extensions of the standard cosmological model, such as modified gravity and primordial non-Gaussianity, taking self-consistently into account. The present constraints on the astrophysics of H i clustering in the treatment of the brightness temperature fluctuations. To understand the boundaries within which results thus obtained can be considered reliable, we examine the robustness of cosmological parameter estimation performed via studies of 21-cm intensity mapping, against our knowledge of the astrophysical processes leading to H i clustering. Modelling of astrophysical effects affects cosmological observables through the relation linking the overall H i mass in a bound object, to the mass of the underlying dark matter halo that hosts it. We quantify the biases in estimates of standard cosmological parameters and those describing modified gravity and primordial non-Gaussianity that are obtained if one misconceives the slope of the relation between H i mass and halo mass, or the lower virial velocity cut-off for a dark matter halo to be able to host H i. Remarkably, we find that astrophysical uncertainties will not affect searches for primordial non-Gaussianity – one of the strongest science cases for H i intensity mapping – despite the signal being deeply linked to the H i bias.
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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 | 0.018 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".