Vertex and energy reconstruction in JUNO with machine learning methods
Abstrak
The Jiangmen Underground Neutrino Observatory (JUNO) is an experiment designed to study neutrino oscillations. Determination of neutrino mass ordering and precise measurement of neutrino oscillation parameters $\sin^2 2\theta_{12}$, $\Delta m^2_{21}$ and $\Delta m^2_{32}$ are the main goals of the experiment. A rich physical program beyond the oscillation analysis is also foreseen. The ability to accurately reconstruct particle interaction events in JUNO is of great importance for the success of the experiment. In this work we present a few machine learning approaches applied to the vertex and the energy reconstruction. Multiple models and architectures were compared and studied, including Boosted Decision Trees (BDT), Deep Neural Networks (DNN), a few kinds of Convolution Neural Networks (CNN), based on ResNet and VGG, and a Graph Neural Network based on DeepSphere. Based on a study, carried out using the dataset, generated by the official JUNO software, we demonstrate that machine learning approaches achieve the necessary level of accuracy for reaching the physical goals of JUNO: $\sigma_E=3\%$ at $E_\text{vis}=1~\text{MeV}$ for the energy and $\sigma_{x,y,z}=10~\text{cm}$ at $E_\text{vis}=1~\text{MeV}$ for the position.
Topik & Kata Kunci
Penulis (23)
Zhenhai Qian
V. Belavin
V. Bokov
R. Brugnera
A. Compagnucci
A. Gavrikov
A. Garfagnini
M. Gonchar
Leyla Khatbullina
Zi-Yuan Li
W. Luo
Y. Malyshkin
Samuele Piccinelli
Ivan Provilkov
Fedor Ratnikov
D. Selivanov
K. Treskov
Andrey Ustyuzhanin
Francesco Vidaich
Z. You
Yu-Mei Zhang
Jiang Zhu
Francesco Manzali
Akses Cepat
- Tahun Terbit
- 2021
- Bahasa
- en
- Total Sitasi
- 52×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1016/j.nima.2021.165527
- Akses
- Open Access ✓