CCAT-prime: RFSoC based readout for frequency multiplexed kinetic inductance detectors
Bibliographic record
Abstract
The Prime-Cam instrument on the Fred Young Submillimeter Telescope (FYST) is expected to be the largest deployment of millimeter and submillimeter sensitive kinetic inductance detectors to date. To read out these arrays efficiently, a microwave frequency multiplexed readout has been designed to run on the Xilinx Radio Frequency System on a Chip (RFSoC). The RFSoC has dramatically improved every category of size, weight, power, cost, and bandwidth over the previous generation readout systems. We describe a baseline firmware design which can read out four independent RF networks each with 500 MHz of bandwidth and 1000 detectors for ∼30 W. The overall readout architecture is a combination of hardware, gateware/firmware, software, and network design. The requirements of the readout are driven by the 850 GHz instrument module of the seven-module Prime-Cam instrument. These requirements along with other constraints which have led to critical design choices are highlighted. Preliminary measurements of the system phase noise and dynamic range are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".