Machine Learning (ML) techniques have been adopted at all levels of applications including experimental design optimization, detector operations and data taking, physics simulations, data reconstruction, and physics inference. Neutrino Physics and Machine Learning (NPML) is dedicated to identify, review, and build future directions for impactful research topics for applying ML techniques in Neutrino Physics.
We invite both individual speakers as well as representatives from a large collaboration in the neutrino community to share the development and applications of ML techniques. Speakers from outside neutrino physics are welcome to make contributions.
Registration: use this registration form (under construction).
Talk Request: please submit your contribution request in this indico page (call for abstracts).
Organization Committee [contact]
C. Adams (ANL), M. Del Tutto (FNAL), P. de Perio (IPMU), F. Drielsma (SLAC), A. Li (UNC), N. Prouse (ICL), K. Terao (SLAC), T. Wongjirad (Tufts)