Internship and thesis proposals
Machine Learning Event Reconstruction in Neutrino Physics

Domaines
High energy physics

Type of internship
Théorique, numérique
Description
Event reconstruction algorithms are used to infer the particle properties, such as energy and direction, based on the photosensor information. Traditional likelihood-based algorithms use several approximations in the modeling of the detector that limit its accuracy and speed, which must be improved for Hyper-K. Several algorithms (DNNs; ResNet CNN, GNN, PointNet, UNet) have been adapted to our particular data format and need to be applied to real physics data. Two positions are available for this project: a) application to CERN particle beam data in the Water Cherenkov Test Experiment, b) application to Super-K cosmic ray and atmospheric neutrino data.
Contact
Michel Gonin
Laboratory : ILANCE - TOKYO - ITL 2014
Team : ILANCE
Team Website
/ Thesis :    Funding :