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Neutrino Physics and Machine Learning (NPML)

10–24 Jul 2020
America/Chicago timezone

Kazuhiro Terao

Session

Day 1 Morning

10 Jul 2020, 10:00

Conveners

Day 1 Morning

  • Kazuhiro Terao (SLAC)
  • Marco Del Tutto (Fermilab)

Presentation materials

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  1. 48. Introduction
    Adam Aurisano (University of Cincinnati), Corey Adams (Argonne National Laboratory), Jianming Bian (University of California, Irvine), Kazuhiro Terao (SLAC), Marco Del Tutto (Fermilab), Nick Prouse (TRIUMF), Patrick de Perio (TRIUMF), Taritree Wongjirad (Tufts University)
    10/07/2020, 10:00
  2. 45. Welcome: High Energy Physics and Machine Learning
    Dr Michael Cooke (U.S. Department of Energy)
    10/07/2020, 10:10
  3. 46. White paper introduction
    Adam Aurisano (University of Cincinnati)
    10/07/2020, 10:25
  4. 34. DL In MicroBooNE
    Taritree Wongjirad (Tufts University)
    10/07/2020, 10:45
    A collaboration/project summary talk

    The MicroBooNE experiment consists of liquid argon time projection chamber(LArTPC) situated in the path of the Booster Neutrino Beam (BNB) at Fermilab. The goals of the experiment are to (1) investigate the observation of an excess of a possible electron-neutrino and anti-neutrino events by the MiniBooNE experiment, (2) measure argon-nucleus cross sections, and (3) perform R&D for LArTPCs. The...

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  5. 5. Using Sparse Convolutional Neural Networks in MicroBooNE
    Ran Itay (WEIZMANN INST.)
    10/07/2020, 11:25
    Individual talk

    The MicroBooNE experiment employs a Liquid Argon Time Projection Chamber (LArTPC) detector to measure sub-GeV neutrino interactions from the muon neutrino beam produced by the Booster Neutrino Beamline at Fermilab. Neutrino oscillation measurements, such as those performed in MicroBooNE, rely on the capability to distinguish between different flavors of neutrino interactions. Deep...

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  6. 10. A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE LArTPC
    Rui An (Illinois Institute of Techonology)
    10/07/2020, 11:50
    Individual talk

    MicroBooNE has accumulated data in a 1E21 POT neutrino beam over five years to test the excess of low energy electron neutrino-like events observed by MiniBooNE. To this end, we have explored the use of a new hybrid analysis chain that includes both conventional and machine learning reconstruction algorithms to identify events with the exclusive 1-proton-1-electron signal topology. The...

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