Étude du potentiel de la détection des antineutrinos pour la surveillance des réacteurs nucléaires à des fins de lutte contre la prolifération.
Bibliographic record
Abstract
The field of applied neutrino physics has shown new developments in the last decade. Indeed, the antineutrinos (ne) emitted by a nuclear power plant depend on the composition of the fuel : thus their detection could be exploited for determining the isotopic composition of the reactor fuel. The International Atomic Energy Agency (IAEA) has expressed its interest in the potentialities of this detection as a new safeguard tool and has created an Ad Hoc working group devoted to this study. Our aim is to determine on the one hand, the current sensitivity reached with the ne detection and on the other hand, the sensitivity required to be useful to the IAEA and then we deduce the required performances required for a cubic meter detector.We will first present the physics on which our feasibility study relies : the neutrinos, the b-decay of the fission products (FP) and their conversion into ne spectra. We will then present our simulation tools : we use a package called MCNP Utility for Reactor Evolution (MURE), initially developed by CNRS/IN2P3 labs to study Generation IV reactors. Thanks to MURE coupled with nuclear data bases, we build the ne spectra by summing the FP contributions. The method is the only one that allows the ne spectra calculations associated to future reactors : we will present the predicted spectra for various innovative fuels. We then calculate the emitted ne associated to various concepts of current and future nuclear reactors in order to determine the sensitivity of the ne probe to various diversion scenarios, taking neutronics into account. The reactor studied are CANadian DeUterium, Pebble Bed Reactor and Fast Breeder Reactor.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".