Improved performance of TES bolometers using digital feedback
Bibliographic record
Abstract
Voltage biased, frequency multiplexed TES bolometers have become a widespread tool in mm-wave astrophysics. However, parasitic impedance and dynamic range issues can limit stability, performance, and multiplexing factors. Here, we present novel methods of overcoming these challenges, achieved through digital feedback, implemented on a Field-Programmable Gate Array (FPGA). In the first method, known as Digital Active Nulling (DAN), the current sensor (e.g. SQUID) is nulled in a separate digital feedback loop for each bolometer frequency. This nulling removes the dynamic range limitation on the current sensor, increases its linearity, and reduces its effective input impedance. Additionally, DAN removes constraints on wiring lengths and maximum multiplexing frequency. DAN has been fully implemented and tested. Integration for current experiments, including the South Pole Telescope, will be discussed. We also present a digital mechanism for strongly increasing stability in the presence of large series impedances, known as Digitally Enhanced Voltage Bias (DEVB).
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".