29 July 2019 to 2 August 2019
Northeastern University
US/Eastern timezone

Electron Neutrino Energy Reconstruction with Convolutional Neural Network

31 Jul 2019, 17:40
20m
Shillman 425 (Northeastern University)

Shillman 425

Northeastern University

Oral Presentation Computing, Analysis Tools, & Data Handling Computing, Analysis Tools, & Data Handling

Speaker

Shiqi Yu (ANL/IIT)

Description

NOvA is a long baseline neutrino oscillation experiment. It is optimized to measure νe appearance and νμ disappearance at the Far Detector in the νμ beam produced by the NuMI facility at Fermilab. NOvA uses a convolutional neural network(CVN) to identify neutrino events in two functionally identical liquid scintillator detectors. A different network, called “Prong-CVN”, has been used to classify reconstructed particles in each event as either lepton or hadron. Within each event, hits are clustered into prongs to recon- struct final state particles and these prongs form the input to this new classifier. Classified particle energies are then used as input to an electron neutrino energy estimator. Improving the resolution and systematic robustness of NOvA’s energy estimator will improve the sensitivity of the oscillation measurements. In this talk, I will present our methods to identify particles with Prong-CVN and the following approach to estimate νe energy for signal events.

Primary author

Shiqi Yu (ANL/IIT)

Presentation Materials