Energy-Resolved Neutron Imaging using a Delay Line Current-Biased Kinetic-Inductance Detector
Bibliographic record
Abstract
Abstract We demonstrate the development of an energy resolved neutron transmission imaging system via a solid-state superconducting detector, called current-biased kinetic-inductance detector (CB-KID). CB-KIDs comprise X and Y superconducting Nb meanderlines with Nb ground plane and a 10B conversion layer, which converts a neutron to two charged particles. High-energy charged particles are able to create quasi-particle hot spots simultaneously in the X and Y meander lines, and thus, the local Cooper pair density in meander lines is reduced temporary. When DC-bias currents are fed into the meander lines, double pairs of voltage pulses are generated at the hot spots and propagate toward both ends of the meander lines as electromagnetic waves. The position of the original hot spot is determined by a difference in arrival times of the two pulses at the two ends for X and Y meander lines, independently. This is so-called the delay-line method, and allows us to reconstruct the two-dimensional neutron transmission image of a test sample with four signal readout lines. We examined the capability of high spatial and energy (wavelength) resolved neutron transmission imaging over the sensor active area of 15 ×15 mm2 for various samples, including biological and metal ones. We also demonstrated the capability for the Bragg edge transmission and an energy-resolved neutron image in which stainless-steel specimens were discriminating from other specimens.
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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 | 0.001 | 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".