Neutrino Physics and Machine Learning (NPML): Lightning Talks

Session

Day 1: Morning

17 Jun 2020, 10:30

Conveners

Day 1: Morning: Introduction

• Kazuhiro Terao (SLAC)
• Taritree Wongjirad (Tufts University)

Day 1: Morning: Lightning Talks

• Kazuhiro Terao (SLAC)
• Taritree Wongjirad (Tufts University)

49. Introduction

Kazuhiro Terao (SLAC)

17/06/2020, 10:30

20. Development of a BDT-based multi-ring sample at the T2K far detector

Trevor Towstego (University of Toronto)

17/06/2020, 10:45

The T2K experiment in Japan studies neutrino oscillations by measuring $\nu_{e}$ appearance and $\nu_{\mu}$ disappearance from a $\nu_{\mu}$ beam using a near and far detector. Super-Kamiokande (SK), a large water Cherenkov detector, acts as the far detector, where charged products of neutrino interactions on water are observed as rings of light. Neutrino oscillation analyses at T2K currently...

3. A Machine Learning Approach to Study the Neutrino Charged-current Interaction on 127I

Mr Peibo An (Duke University)

17/06/2020, 11:00

An inclusive measurement of the cross section of the neutrino charged-current interactions on 127I will help study the quenching of gA , the axial-vector coupling constant, which determines the rate of neutrinoless double beta decays. At the Los Alamos Meson Production Facility (LAMPF), an exclusive measurement was made but with a large statistical error. To make an inclusive and more accurate...

19. Using Sparse Convolutional Neural Networks in MicroBooNE

Ran Itay (WEIZMANN INST.)

17/06/2020, 11:15

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...

15. Detection of Cosmic Muon Spallation Background in LS-Detector Using Machine Learning

Zhenghao Fu (MIT)

17/06/2020, 11:30

Neutrinos are the most abundant but also the most mysterious fermions in the universe. In rare event searches like those for neutrinoless double-beta decay (0νββ), one of major backgrounds is caused by cosmic muon spallation. To remove these background events, a precise method with high efficiency is required to separate them from signal events. Machine learning offers a solution to this...

21. Estimating prediction uncertainties for neutron tagging in Super-Kamiokande

Dr Bryan Zaldívar

Neutrino experiments (particularly Super-Kamiokande) rely on neutron tagging techniques in order to discriminate between neutrinos and antineutrinos. The use of traditional Machine Learning (ML) techniques in order to build data-driven neutron taggers is nowadays a well established procedure which show very good performance. In the language of ML, the idea is to build a classifier which is...