High-resolution imaging by superconducting delay-line detector using 30ps operating readout circuit
Bibliographic record
Abstract
Abstract A current-biased kinetic inductance detector CB-KID was proposed to observe an image of hot spots produced local stimuli on the orthogonal XY meanderlines. Under pulsed neutrons, the CB-KID system has two functions, i.e., not only as a delay-line neutron transmission imager (Role-I) but also as a time-of-flight-spectroscopy instrument (Role-II). When CB-KID operated with a time-to-digital convertor (TDC) of 1-ns temporal resolution with the Kalliope-I readout system, an unsolved issue was that we cannot specify a hot-spot position at a precision of a meander pitch 1.5 μm. A signal velocity inside the detector is very fast as ∼20% of the light velocity enough to allow signal propagation over a longer distance more than several pitches during 1 ns. We developed a new Kalliope-II circuit equipping with a high-resolution TDC (HR-TDC) and a front-end main circuit for continuous readout data acquisition (DAQ) system (AMANEQ) working at a 30-ps temporal resolution. Not only a timestamp t rise at the threshold but also a time-over-threshold (ToT) τ acquired at 30 ps temporal resolution enable us to conduct a delay-time (δ) correction of timestamps t rise of events to yield a transmission image with high contrast. The pixel size of the Kalliope-II imager reached down to 1.5 μm×1.5 μm. It is apparent that the CB-KID sensor works to detect various sorts of stimuli. Unlike other superconducting detectors, CB-KID realized an untrodden 100,000,000-pixel camera over the 15 mm×15 mm area.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".