Neutrino Physics and Machine Learning (NPML): Lightning Talks

Session

Day 1: Afternoon

17 Jun 2020, 13:00

Conveners

Day 1: Afternoon: Lightning Talks

• Jianming Bian (University of California-Irvine)
• Adam Aurisano (University of Cincinnati)

23. A Convolutional Neural Network for Shower Energy Reconstruction in MicroBooNE

Nicholas Kamp

17/06/2020, 13:00

The MicroBooNE experiment consists of a liquid argon time projection chamber (LArTPC) in which ionization electrons created by charged particles traversing the detector are collected by a set of three anode wire planes. The wire readout is combined with the drift time of the ionization electrons to reconstruct the 3D path of the charged particle. Electrons from neutrino interactions will...

24. Machine Learning Applications for Reactor Antineutrino Detection at PROSPECT

Andrea Delgado (Oak RIdge National Laboratory)

17/06/2020, 13:15

PROSPECT is an antineutrino detector located above ground at the High-Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL). The energy spectrum of antineutrinos emitted from the reactors is measured by using a delayed coincidence technique through the inverse-beta-decay reaction (IBD). The ORNL group is currently exploring several applications of machine learning techniques for...

5. Using Convolutional Neural Network for Pulse Shape Discrimination in Liquid Argon

Jacob Daughhetee (University of Tennessee)

17/06/2020, 13:30

The COHERENT collaboration utilizes a suite of detectors to search for CEvNS and associated backgrounds at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory. CENNS-10, a single-phase liquid Ar detector operating since the spring of 2017, seeks to measure the CEvNS process in Ar. Standard pulse-shape discrimination in Ar makes use of the characteristic scintillation emission...

17. Reconstructing 10 GeV-Scale Neutrino Events in IceCube using CNNs

Jessie Micallef (Michigan State University)

17/06/2020, 13:45

The IceCube Neutrino Observatory, which instruments a cubic kilometer of Antarctic ice, aims to detect astrophysical and atmospheric neutrinos. The detector contains 5160 photomultiplier tubes arranged in a 3D hexagonal array, which capture Cherenkov radiation emitted from the daughter particles of neutrino interactions. While IceCube detects astrophysical neutrinos in the TeV-PeV energy...

8. Reconstructing Michel Electrons in ICARUS with Deep Neural Networks

Laura Domine (Stanford University)

17/06/2020, 14:00

Deep neural networks (DNN) enabled countless breakthroughs in the fields of artificial intelligence and computer vision and they have been successfully applied to the data reconstruction of Liquid Argon Time Projection Chambers (LArTPC), which offer high resolution (~3mm/pixel) 2D or 3D imaging of charged particles' trajectories. The ICARUS detector is a large-scale (760-ton) LArTPC far...

13. Particle Clustering in LArTPCs with Embedding Learning Convolutional Neural Networks

Dae Koh (SLAC)

17/06/2020, 14:15

In this poster, we present a fast and scalable deep learning algorithm for particle clustering in liquid argon time projection chamber (LArTPC) data, as part of the machine learning based reconstruction effort at SLAC. Particle clustering refers to the task of grouping image pixels into particle instances, which is regarded as one of the most challenging tasks in LArTPC data reconstruction....