NeutralNet: an application of deep neural networks to pulse shape discrimination for use with accelerator-based neutron sources
Bibliographic record
Abstract
Recent works have implemented machine learning based solutions for many complex classification tasks including pulse shape discrimination in radiation detection. The present work aims to advance the application of machine learning to pulse shape discrimination in neutron detection. A machine learning based neutron-gamma discrimination technique is investigated for various neutron energy distributions produced from DD, DT, (α,n), and spontaneous fission neutron sources. Comprehensive investigations on the training data generation techniques, the impact of the PMT bias, and the discrimination performance are conducted. With the increase of the PMT bias voltage, the neutron classification performance peaked at 1500 with 81 % of validation neutrons being identified at a false positive rate of 1E-6 while the further bias increase led to a notable degradation in performance. The unsatisfactory classification performance encountered when training off of one neutron source type and classifying neutrons from the other source types was greatly improved with the application of the transfer learning techniques. The remaining variation in the performance was accounted for by the energy dependence of the neutron classification. It was demonstrated that at the 1E-6 FPR specificity level, the events within the region of overlap for neutron and photon populations could be separated, down to a detected energy of 30 keVee. An overall intrinsic neutron detection efficiency of 12.5 % was achieved for the 252 Cf neutron source at a false positive rate of 1E-6.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".