High dimensional beam inference II: inference of a perturbed HERA beam from simulated visibility data
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Accurate beam modelling is important in many radio astronomy applications. In this paper, we focus on beam modelling for 21-cm intensity mapping experiments using radio interferometers, though the techniques also apply to single dish experiments with small modifications. In 21-cm intensity mapping, beam models are usually determined from highly detailed electromagnetic simulations of the receiver system. However, these simulations are expensive, and therefore have limited ability to describe practical imperfections in the beam pattern. We present a fully analytic Bayesian inference framework to infer a beam pattern from the interferometric visibilities assuming a particular sky model and that the beam pattern for all elements is identical, allowing one to capture deviations from the ideal beam for relatively low computational cost. We represent the beam using a sparse Fourier-Bessel basis on a projection of the hemisphere to the unit disc, but the framework applies to any linear basis expansion of the primary beam. We test the framework on simulated visibilities from an unpolarized sky, ignoring mutual coupling of array elements. We successfully recover the simulated, perturbed power beam when the sky model is perfect. Briefly exploring sky model inaccuracies, we find that beam inferences are sensitive to them, so we suggest jointly modelling uncertainties in the sky and beam in related inference tasks.
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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 it