neutrino-dnn

Deep-learning applied to neutrino property reconstruction

Goal

To reconstruct neutrino properties from an incoming signal using deep-learning & neural networks. The network is to be trained and evaluated on simulated data.

Author

• The author of this project is Sigfrid Stjärnholm (sigge.stjarnholm@gmail.com).
• The project is performed as part of a Bachelor thesis at Uppsala University, Sweden, in the spring of 2021.
• The project supervisor is Christian Glaser, The Division of High Energy Physics, Department of Physics and Astronomy, Uppsala University, Sweden.