Coherent Multibeam Arrays Using a Cold Aperture Stop
Bibliographic record
Abstract
To increase the mapping speed of a given area-of-sky, multibeam heterodyne arrays may be used. Since typical heterodyne arrays are spatially arranged sparsely at approximately 4·Nyquist sampling (i.e., two full-width-half-maximum beam widths), many pointings are required to sample fully the area of interest. A cold aperture stop may be used to increase the packing density of the detectors, which results in a denser instantaneous spatial sampling on-sky. Combining reimaging optics with the cold stop, good aperture efficiency can be obtained. As expected, however, a significant amount of power is truncated at the stop and the surrounding baffling. We analyze the consequence of this power truncation and explore the possibility of using this layout for coherent detection as a multibeam feed. We show that for a fixed area-of-sky, a “twice-Nyquist” spatial sampling arrangement may improve the normalized point source mapping speed when the system noise temperature is dominated by background or atmospheric contribution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".