Likelihood and Deep Learning Analysis of the electron neutrino event sample at Intermediate Water Cherenkov Detector (IWCD) of the Hyper-Kamiokande experiment
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
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2024
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| _version_ | 1866910946657566720 |
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| author | Mondal, T. Prouse, N. W. de Perio, P. Hartz, M. Bose, D. |
| author_facet | Mondal, T. Prouse, N. W. de Perio, P. Hartz, M. Bose, D. |
| contents | Hyper-Kamiokande (Hyper-K) is a next-generation long baseline neutrino experiment. One of its primary physics goals is to measure neutrino oscillation parameters precisely, including the Dirac CP violating phase. As conventional $ν_μ$ beam generates from the J-PARC neutrino baseline contains only 1.5$\%$ of $ν_{e}$ interaction of total, it is challenging to measure $ν_{e}/\barν_{e}$ scattering cross-section on nuclei. To reduce these systematic uncertainties, IWCD will be built to study neutrino interaction rates with higher precision. Simulated data comprise $ν_{e}CC0π$ as the main signal with NC$π^{0}$ and $ν_μCC$ are major background events. To reduce the backgrounds initially, a log-likelihood-based reconstruction algorithm to select candidate events was used. However, this method sometimes struggles to distinguish $π^{0}$ events properly from electron-like events. Thus, a Machine Learning-based framework has been developed and implemented to enhance the purity and efficiency of $ν_{e}$ events. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09562 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Likelihood and Deep Learning Analysis of the electron neutrino event sample at Intermediate Water Cherenkov Detector (IWCD) of the Hyper-Kamiokande experiment Mondal, T. Prouse, N. W. de Perio, P. Hartz, M. Bose, D. High Energy Physics - Experiment Instrumentation and Detectors Hyper-Kamiokande (Hyper-K) is a next-generation long baseline neutrino experiment. One of its primary physics goals is to measure neutrino oscillation parameters precisely, including the Dirac CP violating phase. As conventional $ν_μ$ beam generates from the J-PARC neutrino baseline contains only 1.5$\%$ of $ν_{e}$ interaction of total, it is challenging to measure $ν_{e}/\barν_{e}$ scattering cross-section on nuclei. To reduce these systematic uncertainties, IWCD will be built to study neutrino interaction rates with higher precision. Simulated data comprise $ν_{e}CC0π$ as the main signal with NC$π^{0}$ and $ν_μCC$ are major background events. To reduce the backgrounds initially, a log-likelihood-based reconstruction algorithm to select candidate events was used. However, this method sometimes struggles to distinguish $π^{0}$ events properly from electron-like events. Thus, a Machine Learning-based framework has been developed and implemented to enhance the purity and efficiency of $ν_{e}$ events. |
| title | Likelihood and Deep Learning Analysis of the electron neutrino event sample at Intermediate Water Cherenkov Detector (IWCD) of the Hyper-Kamiokande experiment |
| topic | High Energy Physics - Experiment Instrumentation and Detectors |
| url | https://arxiv.org/abs/2411.09562 |