ALL-SKY INTERFEROMETRY WITH SPHERICAL HARMONIC TRANSIT TELESCOPES
Bibliographic record
Abstract
In this paper, we describe the spherical harmonic transit telescope through the use of a novel formalism for the analysis of transit radio telescopes. This all-sky approach bypasses the curved-sky complications of traditional interferometry and so is particularly well-suited to the analysis of wide-field radio interferometers. It enables compact and computationally efficient representations of the data and its statistics, which allow new ways of approaching important problems like map-making and foreground removal. In particular, we show how it enables the use of the Karhunen-Loève transform as a highly effective foreground filter, suppressing realistic foreground residuals for our fiducial example by at least a factor 20 below the 21 cm signal, even in highly contaminated regions of the sky. This is despite the presence of the mode-mixing inherent in real-world instruments with frequency-dependent beams. We show, using Fisher forecasting, that foreground cleaning has little effect on power spectrum constraints compared to hypothetical foreground-free measurements. Beyond providing a natural real-world data analysis framework for 21 cm telescopes now under construction and future experiments, this formalism allows accurate power spectrum forecasts to be made that include the interplay of design constraints and realistic experimental systematics with 21st century 21 cm science.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".