Role of deexcitation in the final-state interactions of protons in neutrino-nucleus interactions
Bibliographic record
Abstract
Present and next generation of long-baseline accelerator experiments are bringing the measurement of neutrino oscillations into the precision era with ever-increasing statistics. One of the most challenging aspects of achieving such measurements is developing relevant systematic uncertainties in the modeling of nuclear effects in neutrino-nucleus interactions. To address this problem, state-of-the-art detectors are being developed to extract detailed information about all particles produced in neutrino interactions. To fully profit from these experimental advancements, it is essential to have reliable models of propagation of the outgoing hadrons through nuclear matter able to predict how the energy is distributed between all the final-state observed particles. In this article, we investigate the role of nuclear deexcitation in neutrino-nucleus scattering using two Monte Carlo cascade models: NuWro and INCL coupled with the deexcitation code ABLA. The ablation model ABLA is used here for the first time to model deexcitation in neutrino interactions. As input to ABLA, we develop a consistent simulation of nuclear excitation energy tuned to electron-scattering data. The paper includes the characterization of the leading proton kinematics and of the nuclear cluster production during cascade and deexcitation. The observability of nuclear clusters as vertex activity and their role in a precise neutrino energy reconstruction is quantified.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".