Improved Foreground Modelling for Bayesian 21 cm Power Spectrum Estimation with BayesEoR
Bibliographic record
Abstract
Estimating the power spectrum (PS) of the redshifted 21 cm signal from the Epoch of Reionization (EoR) has proven difficult due to the presence of bright astrophysical foregrounds (FGs). Existing techniques to mitigate FGs during PS estimation under-utilize the data and do not properly account for the covariance between the observed EoR and FG signals. We are developing a Bayesian framework, BayesEoR, that jointly models the instrument, FGs, and 21 cm signal and marginalizes over their uncertainties to enable extraction of statistically robust and unbiased estimates of the EoR PS. In this paper, we present a brief overview of our approach to Bayesian PS estimation and the results of a recent study in which we used BayesEoR to analyze a set of detailed simulations containing mock EoR and realistic FG signals. Due to computational constraints, the forward model of the instrument in BayesEoR, which involves application of the instrument primary beam in the image domain, is restricted to model a subset of the sky. We find that, when the sky emission outside of the modelled domain is downweighted by the beam at the level of the dynamic range between the EoR and FGs, BayesEoR can model visibilities which see the whole sky using only a subset of the sky. We also present several techniques that can be used to mitigate FG contamination during EoR PS estimation while ameliorating computational costs.
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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".