Improving Neutrino Oscillation Measurements through Event Classification
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913023361286144 |
|---|---|
| author | Ellis, Sebastian A. R. Hackett, Daniel C. Li, Shirley Weishi Machado, Pedro A. N. Tame-Narvaez, Karla |
| author_facet | Ellis, Sebastian A. R. Hackett, Daniel C. Li, Shirley Weishi Machado, Pedro A. N. Tame-Narvaez, Karla |
| contents | Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well established that different interaction channels produce systematically varying amounts of missing energy and therefore yield different reconstruction performance--information that standard calorimetric approaches do not exploit. We introduce a strategy that incorporates this structure by classifying events according to their underlying interaction type prior to energy reconstruction. Using supervised machine-learning techniques trained on labeled generator events, we leverage intrinsic kinematic differences among quasi-elastic scattering, meson-exchange current, resonance production, and deep-inelastic scattering processes. A cross-generator testing framework demonstrates that this classification approach is robust to microphysics mismodeling and, when applied to a simulated DUNE $ν_μ$ disappearance analysis, yields improved accuracy and sensitivity at the 10-20% level. These results highlight a practical path toward reducing reconstruction-driven systematics in future oscillation measurements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11938 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Neutrino Oscillation Measurements through Event Classification Ellis, Sebastian A. R. Hackett, Daniel C. Li, Shirley Weishi Machado, Pedro A. N. Tame-Narvaez, Karla High Energy Physics - Phenomenology Artificial Intelligence Machine Learning High Energy Physics - Experiment Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well established that different interaction channels produce systematically varying amounts of missing energy and therefore yield different reconstruction performance--information that standard calorimetric approaches do not exploit. We introduce a strategy that incorporates this structure by classifying events according to their underlying interaction type prior to energy reconstruction. Using supervised machine-learning techniques trained on labeled generator events, we leverage intrinsic kinematic differences among quasi-elastic scattering, meson-exchange current, resonance production, and deep-inelastic scattering processes. A cross-generator testing framework demonstrates that this classification approach is robust to microphysics mismodeling and, when applied to a simulated DUNE $ν_μ$ disappearance analysis, yields improved accuracy and sensitivity at the 10-20% level. These results highlight a practical path toward reducing reconstruction-driven systematics in future oscillation measurements. |
| title | Improving Neutrino Oscillation Measurements through Event Classification |
| topic | High Energy Physics - Phenomenology Artificial Intelligence Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2511.11938 |