A Machine Learning Approach to a Multidetector Array Response Function for Nuclear Search
Bibliographic record
Abstract
During nuclear search operations, the localization of radioactive sources can be a time-consuming process that requires mapping relative radiation intensity in a large area to determine the position of a source. This article introduces the use of machine learning, specifically a temporal convolutional network (TCN), to estimate the direction between a detector array and a static <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">137</sup> Cs source. This application of machine learning provides a directional vector in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\pi $ </tex-math></inline-formula> with a 90% confidence of 5.6° and a 99% confidence within 11.2°. With the use of low-cost NaI(Tl) detectors, the effects of self-shielding within the array creates gamma-ray shadows depending on the orientation to the source. Using the convolved detector array response function, we apply supervised machine learning with a neural network to predict a unit vector that points toward the observed source. The directional vector is expected to reduce search times once implemented in future work.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.001 | 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 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".