A digital beamformer for the advanced focal array demonstrator (AFAD)
Bibliographic record
Abstract
Phased array feeds (PAFs) are an active research area in radio astronomy, as they offer potential advantages over traditional single-pixel feeds. Their key advantage is increased field of view and survey speed, however they also permit tailoring the antenna beam for Ae=Tsys, or other objectives such as attenuating strong radio frequency interference (RFI). A primary research goal is to improve the noise temperature performance of a PAF over comparable single-pixel feeds. In this work we have constructed a small 16-element digital beamformer with 384 MHz of bandwidth to evaluate the performance of NRCs Advanced Focal Array Demonstrator (AFAD) operating from 750 to 1500 MHz. We compare measured sensitivity results to previous measurements made with an analog beamformer. The digital beamformer is implemented using NRCs Kermode platform, a Virtex6-based compute blade. We take a standards-based approach, using the AdvancedTCA (ATCA) form factor for the Kermode board, ANSI/VITA-49.0 framing for all chip-to-chip and chip-to-host communications, and AXI4-Stream format for all internal datapaths. The Kermode system can be expanded with a standard ATCA full-mesh backplane to support up to 128 inputs with over 1 GHz of bandwidth. This expanded capability will ultimately be used to evaluate the performance of the full 96-element AFAD PAF mounted on a re ector antenna. To achieve this goal, we are well into developing a digitizer system that will handle at least 96 elements with up to 1.5 GHz of bandwidth per element. We present an overview of the digitizer system in the context of the PAF beamformer system, and provide an update on the progress to date.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".