2019/10/15 by Shiqi Yu, Yu, Shiqi
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Instrumentation and Detectors (physics.ins-det) #Machine Learning (cs.LG) #cs.LG #hep-ex #physics.ins-det
paper · pdf · doi:10.48550/arxiv.1910.06953
Talk presented at the 2019 Meeting of the Division of Particles and Fields of the American Physical Society (DPF2019), July 29 - August 2, 2019, Northeastern University, Boston, C1907293
arxiv created 2019/10/29 · arxiv updated 2019/10/30
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 (CNN) to identify neutrino events in two functionally identical liquid scintillator detectors. A different network, called prong-CNN, has been used to classify reconstructed particles in each event as either lepton or hadron. Within each event, hits are clustered into prongs to reconstruct final-state particles and these prongs form the input to this prong-CNN 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 parameters measurement. This paper describes the methods to identify particles with prong-CNN and the following approach to estimate νe energy for signal events.