Interferometric H <scp>i</scp> intensity mapping: perturbation theory predictions and foreground removal effects
Bibliographic record
Abstract
ABSTRACT We provide perturbation theory predictions for the H i intensity mapping power spectrum multipoles using the Effective Field Theory of Large Scale Structure, which should allow us to exploit mildly non-linear scales. Assuming survey specifications typical of proposed interferometric H i intensity mapping experiments like Canadian Hydrogen Observatory and Radio transient Detector and PUMA, and realistic ranges of validity for the perturbation theory modelling, we run mock full shape Markov chain Monte Carlo (MCMC) analyses at z = 0.5, and compare with Stage-IV optical galaxy surveys. We include the impact of 21cm foreground removal using simulations-based prescriptions, and quantify the effects on the precision and accuracy of the parameter estimation. We vary 11 parameters in total: three cosmological parameters, seven bias and counter terms parameters, and the H i brightness temperature. Amongst them, the four parameters of interest are: the cold dark matter density, ωc, the Hubble parameter, h, the primordial amplitude of the power spectrum, As, and the linear H i bias, b1. For the best-case scenario, we obtain unbiased constraints on all parameters with $\lt 3{{\ \rm per\ cent}}$ errors at $68{{\ \rm per\ cent}}$ confidence level. When we include the foreground removal effects, the parameter estimation becomes strongly biased for ωc, h, and b1, while As is less biased (&lt;2σ). We find that scale cuts $k_{\rm min} \ge 0.03 \ h\,\mathrm{Mpc}^{-1}$ are required to return accurate estimates for ωc and h, at the price of a decrease in the precision, while b1 remains strongly biased. We comment on the implications of these results for real data analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".