Homogeneity of neutron transmission imaging over a large sensitive area with a four-channel superconducting detector
Bibliographic record
Abstract
Abstract We previously proposed a method to detect neutrons by using a current-biased kinetic inductance detector (CB-KID), where neutrons are converted into charged particles using a 10 B conversion layer. The charged particles are detected based on local changes in kinetic inductance of X and Y superconducting meanderlines under a modest DC bias current. The system uses a delay-line method to locate the positions of neutron- 10 B reactions by acquiring the four arrival timestamps of signals that propagate from hot spots created by a passing charged particle to the end electrodes of the meanderlines. Unlike conventional multi-pixel imaging systems, the CB-KID system performs high spatial resolution imaging over a 15 mm × 15 mm sensitive area using only four channel readouts. Given the large sensitive area, it is important to check the spatial homogeneity and linearity of detected neutron positions when imaging with CB-KID. To this end we imaged a pattern of 10 B dot absorbers with a precise dot pitch to investigate the spatial homogeneity of the detector. We confirmed the spatial homogeneity of detected dot positions based on the distribution of measured dot pitches across the sensitive area of the detector. We demonstrate potential applications of the system by taking a clear transmission image of tiny metallic screws and nuts and a ladybug. The image was useful for characterizing the ladybug noninvasively. Detection efficiencies were low when the detector was operated at 4 K, so we plan to explore raising the operating temperature towards the critical temperature of the detector as a means to improve counting rates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".