Strengths and limitations of cosmic-ray muon tomography for nuclear disarmament verification application
Bibliographic record
Abstract
Nuclear disarmament verification (NDV), which is the use of technologies and procedures to verify nuclear weapons reductions without the transfer of sensitive information, is currently a major worldwide field of work. The work presented here demonstrates experimentally the usefulness of muon tomography for the verification of a mock-up of a nuclear warhead. Experimental results are extended further with Monte Carlo simulations to predict results and draw conclusions in the case of enclosures containing a nuclear weapon core. Muon tomography has proven to be effective in the detection of high-density and high-atomic-number materials, even when they are surrounded by heavy shielding such as lead. Although the technique can determine the presence of a high-density and high-Z core, our muon tomography system and analysis algorithm have limited angular resolution and therefore do not provide information on the nature of the material. To overcome this limitation and to mitigate risks associated with swapping a nuclear weapon core with tungsten, gamma rays and neutron techniques are proposed. These are shown to be complementary techniques for NDV applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".