Neutron transmission imaging system with a superconducting kinetic inductance detector
Bibliographic record
Abstract
Abstract We optimized the design and operating conditions of our superconducting neutron detector to improve spatial resolution. We obtained the best spatial resolution of 10 μm when a Gd Siemens star pattern was mounted in close contact with the detector. Although there is a trade-off between a spatial resolution and an easiness of replacing samples, we built our superconducting neutron imaging system for measuring in both the room-temperature samples with a proper collimation ratio L/D for achieving a reasonable spatial resolution and a cryogenic temperature with the best spatial resolution for certain purposes. In this study, we obtained neutron transmission images of various samples when they were cooled down with the superconducting neutron detector. We compared the effect of a different sample-detector distance on a spatial resolution when the samples were placed either at cryogenic temperature or at room temperature. We also confirmed that our CB-KID sensor was able to observe the neutron transmission coefficient over wider energies of pulsed neutrons. We found the appearance of clear Bragg dips by the measurements of natural FeS2 single crystals and succeeded in mapping the distribution of differently-oriented crystals by choosing several Bragg dips of the FeS2 crystals to compose the transmission images.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".