Experimental verification of radio frequency interference mitigation with a focal plane array feed
Bibliographic record
Abstract
We demonstrate the use of spatial filtering algorithms for radio frequency interference (RFI) mitigation in conjunction with a focal plane array of electrically small elements. The array consists of a seven‐element hexagonal arrangement of thickened dipole antennas with 1600 MHz designed center frequency backed by a circular ground plane at the focal plane of a 3 m parabolic reflector. Rooftop‐mounted signal sources were used to simulate a weak signal of interest at boresight and a strong, broadband interferer in the reflector sidelobes. Using an adaptive beamformer, the amplitude of the interfering signal was reduced sufficiently to recover the signal of interest. For an interference to noise ratio of 15 dB as measured at the center array element, the interferer was suppressed to the level of the fluctuations of the 10‐s integrated noise floor (the minimum detectable signal level was interference‐limited and no longer decreased after 10 s integration). Similar cancellation performance was demonstrated for a nonstationary interferer moving at an angular velocity of 0.1° per second. Pattern rumble due to beamformer adaptation was observed and quantified. For a moving RFI source, the degree of pattern rumble was found to be unacceptably large in terms of its effects on the maximum stable integration time and receiver sensitivity. An array feed with more elements together with specialized signal processing algorithms designed to suppress pattern rumble will likely be required in order to use adaptive spatial filtering for astronomical observations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".