The characterisation of a PID feedback-controlled array of TES-bolometers in a double-Fourier interferometry testbed
Bibliographic record
Abstract
Double-Fourier interferometry (DFI) from a space-based platform provides a path to achieve broadband imaging spectroscopy in the far-infrared with sub-arcsecond angular resolution. To provide further study of the technique and improve its technology readiness, we have constructed a laboratory-based DFI testbed. This instrument is coupled to a custom array of 25 feedback-controlled transition-edge sensor (TES) bolometers. We present the results of characterisation experiments to optimise the detector system for laboratory-based experiments involving the DFI testbed. We demonstrate that tuning the proportional-integral-derivative (PID) feedback control loops of the detectors and the timing of the multiplexed measurement process can modify the detector array's noise performance and speed of response to optical modulation for this purpose. From these, we have determined a set of optimised detector settings that reduce spectral noise in the spatial-spectral interferometer by 37-75%. In addition, we present further thermal characterisation of the detector array.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".