Using exotic atoms to keep borders safe
Bibliographic record
Abstract
Muons, created by a particle accelerator, can be used to scan cargo for special nuclear materials (SNM). These muons have a sufficiently long lifetime and are penetrating enough that they can be used to actively scan cargo to ensure the non-proliferation of SNM. A set of "proof-of-concept" experiments have been performed to show that active muon analysis can be used. Experiments were performed at high intensity, medium energy particle accelerators (TRIUMF and PSI). Negative muons form exotic atoms with one electron replaced by the muon. Since the muon is captured in an excited state, it will give off x-rays which can be detected by high purity germanium detectors. The characteristic x-ray spectrum can be potentially used to identify nuclides. The muonic x-rays corresponding to the SNM of interest have been measured, even with the use of various shielding configurations composed of lead, iron, polyethylene, or fibreglass. These preliminary results show that muon scanning systems can be successfully used to find shielded SNM, helping to ensure the safety of all citizens.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".