Machine learning for the cosmic ray inspection and passive tomography project (CRIPT)
Bibliographic record
Abstract
Muons, which are produced naturally in the upper atmosphere, can be used to scan cargo for special nuclear materials (SNM). Preliminary simulated results show that detecting the presence of these materials can be accomplished by measuring the scattering of cosmic ray muons. Machine learning tools have been used on these data to classify it as SNM or not. The muon exists long enough, and is penetrating enough, that it can be used to passively scan cargo to detect SNM. By measuring the deflection angles of muons after they exit a container, one can determine whether or not SNM are present. Different detector approaches have been evaluated by considering the performance, cost, and robustness of several technologies. Simulations have been performed to help design the detectors and to determine the effectiveness of the proposed techniques. Realistic cargo containers have been simulated. Two types of techniques can be used to determine whether the cargo containers contain SNM. More traditional methods use an expert system which uses knowledge of physics to compute physical information about the cargo. The other approach is to use Machine Learning classifiers, which can be used to determine if the cargo contains SNM. These techniques include the following algorithms: decision trees, neural networks, special vector machines, and k nearest neighbours. Preliminary results from the two approaches to classification have been obtained and will be discussed in the paper.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".