Neutrino Physics and Machine Learning (NPML)

Kazuhiro Terao (SLAC)

The goal of the workshop is to collect a summary of Machine Learning (ML) techniques that have been explored as well as promising new methods for existing and future research goals in the field of neutrino physics, then to form the current and future vision of the ML techniques R&D for maximizing the impact on the science output. We invite any ML practitioners in the neutrino physics community and beyond to exchange ideas and share experience.

ML have been adopted at all levels of applications including the simulations, data reconstruction, and end-user physics analyses. We invite both individual speakers as well as representative figures from a large collaboration in the neutrino community to share the development of ML techniques.

The Neutrino 2020 Satellite ML Workshop is a preceding event to this workshop, which output will be included in the discussion of this workshop.

Please send your talk request at each event's indico page separately!

Please register for either of two events using the registration form.


Organization Committee [contact]

C. Adams (ANL), A. Aurisano (Cincinnati), J. Bian (UC Irvine), A. Friedland (SLAC), A. Konaka (TRIUMF), P. de Perio (TRIUMF), N. Prouse (TRIUMF), F. Psihas (FNAL), K. Terao (SLAC), M. Del Tutto (FNAL), T. Wongjirad (Tufts)