Kinetic inductance neutron detector operated at near critical temperature
Bibliographic record
Abstract
Abstract We previously succeeded in constructing and demonstrating the capability of a neutron imaging system based on a superconducting current-biased kinetic inductance detector (CB-KID). In the present work, we systematically studied the characteristics of the superconducting neutron detector to improve the spatial resolution and detection efficiency. We found that the number of neutron detection events with CB-KID remarkably increased when the detector temperature increased from 4 K to the critical temperature T c . We observed systematic changes of neutron signals as a function of the detector temperature from 4 K to T c . We evaluated the detection efficiency of the CB-KID detector, and compared with PHITS Monte Carlo simulations, which modeled the sequential physical processes for the 10 B(n,α) 7 Li reaction, the transport dynamics, and the energy deposition by particles including neutrons, 4 He particles, 7 Li particles, photons, and electrons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".