Development of TRL5 Space Qualified Hardware for Tuning, Biasing, and Readout of Kilopixel TES Bolometer Arrays
Bibliographic record
Abstract
The next generation of space-based mm-wave telescopes, such as JAXA's LiteBIRD mission, require focal planes with thousands of detectors in order to achieve their science goals. Digital frequency-domain multiplexing (dfmux) techniques allow detector counts to scale without a linear growth in wire harnessing, sub-Kelvin refrigerator loads, and other scaling problems. In this paper, we introduce Technology Readiness Level 5 (TRL5) electronics suitable for biasing and readout of LiteBIRD's Transition Edge Sensor (TES) bolometers using dfmux techniques. These electronics sit between the spacecraft's payload computer and the cryogenic focal plane, and provide detector biasing, tuning, and readout interfaces between these detectors and the spacecraft's on-board storage. We describe the overall architecture of the electronics, including functional decomposition into modules, the numerology and interconnection of these modules, and their internal and external interfaces. We describe performance measurements to date, including power consumption, thermal performance, and mass, volume, and reliability estimates. This paper is a companion piece to a description of the electronics' on-board Field-Programmable Gate Array (FPGA) firmware.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".