Modeling and Testing a Time-of-Flight Neutron Emission Spectrometer for General Fusion
Bibliographic record
Abstract
General Fusion is building a prototype fusion machine as part of the Fusion Demonstration Program (FDP) in Culham, U.K. using principles of magnetic target fusion. A primary goal of the FDP is to reach an ion temperature of 10 keV, and making a measurement of this temperature will be a technological challenge. The proposed solution to this measurement in a time-of-flight neutron emission spectrometer. Deuterium-deuterium fusion reaction produce monoenergetic 2.45 MeV neutrons, but the temperature of the plasma causes the energy in the lab frame to have a dispersion with standard deviation proportional to the square root of the temperature in keV. A neutron spectrometer built from two layers of plastic scintillator can measure neutron energies if the neutrons interact in both layers by recording the scattering angle and time of flight between the two interaction points. This spectrometer will be a challenge to design due to the low total neutron count and high rate, so extensive modelling and simulation of the neutron spectrometer’s response are essential to ensuring a precise enough measurement of the plasma temperature can be made. This poster will describe the design and testing plan of the neutron spectrometer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".