Nucleon binding energy and transverse momentum imbalance in neutrino-nucleus reactions
Bibliographic record
Abstract
We have measured new observables based on the final state kinematic imbalances in the mesonless production of ${\ensuremath{\nu}}_{\ensuremath{\mu}}+A\ensuremath{\rightarrow}{\ensuremath{\mu}}^{\ensuremath{-}}+p+X$ in the MINER\ensuremath{\nu}A tracker. Components of the muon-proton momentum imbalances parallel ($\ensuremath{\delta}{p}_{\mathrm{Ty}}$) and perpendicular ($\ensuremath{\delta}{p}_{\mathrm{Tx}}$) to the momentum transfer in the transverse plane are found to be sensitive to the nuclear effects such as Fermi motion, binding energy, and non-quasielastic (QE) contributions. The QE peak location in $\ensuremath{\delta}{p}_{\mathrm{Ty}}$ is particularly sensitive to the binding energy. Differential cross sections are compared to predictions from different neutrino interaction models. The Fermi gas models presented in this study cannot simultaneously describe features such as QE peak location, width, and the non-QE events contributing to the signal process. Correcting the genie's binding energy implementation according to theory causes better agreement with data. Hints of proton left-right asymmetry are observed in $\ensuremath{\delta}{p}_{\mathrm{Tx}}$. Better modeling of the binding energy can reduce the bias in neutrino energy reconstruction, and these observables can be applied in current and future experiments to better constrain nuclear effects.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".