High Spatial Resolution Neutron Transmission Imaging Using a Superconducting Two-Dimensional Detector
Bibliographic record
Abstract
Neutron imaging is one of the most powerful tools for nondestructive inspection owing to the unique characteristics of neutron beams, such as high permeability for many heavy metals, high sensitivity for certain light elements, and isotope selectivity owing to a specific nuclear reaction between an isotope and neutrons. In this study, we employed a superconducting detector, current-biased kinetic-inductance detector (CB-KID) for neutron imaging using a pulsed neutron source. We employed the delay-line method, and high spatial resolution imaging with only four reading channels was achieved. We also performed wavelength-resolved neutron imaging by the time-of-flight method for the pulsed neutron source. We obtained the neutron transmission images of a Gd–Al alloy sample, inside which single crystals of GdAl$_3$were grown, using the delay-line CB-KID. Single crystals were well imaged, in both shapes and distributions, throughout the Al–Gd alloy. We identified Gd nuclei via neutron transmissions that exhibited characteristic suppression above the neutron wavelength of 0.03 nm. In addition, the$^{155}$Gd resonance dip, a dip structure of the transmission caused by the nuclear reaction between an isotope and neutrons, was observed even when the number of events was summed over a limited area of 15$\mu$m × 12$\mu$m. Gd selective imaging was performed using the resonance dip of$^{155}$Gd, and it showed clear Gd distribution even with a limited neutron wavelength range of 1 pm.
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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".