Reconstruction of event characteristics with the use of modern machkine learning techniques for Jiangmen Underground Neutrino Observatory being constructed in China.
Supervisor’s specific requirements
Basic Knowledge of Nuclear and Elementary Particle Physics
Strong Programming and Data Analysis Skills (Python, NumPy, SciPy)
Understanding of Machine Learning Principles and Experience of their Usage
Extra Financial Support from JINR
Research highlights
Work in an international collaboration
The most advanced detector of the kind
Opportunity to join the experiment at the time of its launch (in 2022)
Main publications
F. An. et al. (JUNO collaboration), “Neutrino Physics with JUNO”, Journal of Physics G: Nuclear and Particle Physics, 43(3), 2016. [arXiv: 1507.05613]
Angel Abusleme et al. (JUNO collaboration), “TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy”, 2020
M. G. Aartsen et al., “Combined sensitivity to the neutrino mass ordering with JUNO, the IceCube Upgrade, and PINGU”, 2020
V B Petkov et al., “Baksan large volume scintillation telescope: a current status”, Journal of Physics Conference Series, 2019
M G Aartsen et al., “Combined sensitivity to the neutrino mass ordering with JUNO, the IceCube Upgrade, and PINGU”, 2019