Name: Neutrino Physics and Machine Learning (NPML): Lightning Talks
Start: 2020-06-17T10:00:00-05:00
End: 2020-06-19T16:00:00-05:00
Location: No location set

Neutrino Physics and Machine Learning (NPML): Lightning Talks

from Wednesday 17 June 2020 (10:00) to Friday 19 June 2020 (16:00)

Monday 15 June 2020
Tuesday 16 June 2020
Wednesday 17 June 2020
10:30 Introduction
Introduction
10:30 - 10:45
Contributions

10:30 Introduction - Kazuhiro Terao (SLAC)
10:45 Lightning Talks
Lightning Talks
10:45 - 12:00
Contributions

10:45 Development of a BDT-based multi-ring sample at the T2K far detector - Trevor Towstego (University of Toronto)

11:00 A Machine Learning Approach to Study the Neutrino Charged-current Interaction on 127I - Peibo An (Duke University)

11:15 Using Sparse Convolutional Neural Networks in MicroBooNE - Ran Itay (WEIZMANN INST.)

11:30 Detection of Cosmic Muon Spallation Background in LS-Detector Using Machine Learning - Zhenghao Fu (MIT)
12:00

12:00 - 13:00
13:00 Lightning Talks
Lightning Talks
13:00 - 14:30
Contributions

13:00 A Convolutional Neural Network for Shower Energy Reconstruction in MicroBooNE - Nicholas Kamp

13:15 Machine Learning Applications for Reactor Antineutrino Detection at PROSPECT - Andrea Delgado (Oak RIdge National Laboratory)

13:30 Using Convolutional Neural Network for Pulse Shape Discrimination in Liquid Argon - Jacob Daughhetee (University of Tennessee)

13:45 Reconstructing 10 GeV-Scale Neutrino Events in IceCube using CNNs - Jessie Micallef (Michigan State University)

14:00 Reconstructing Michel Electrons in ICARUS with Deep Neural Networks - Laura Domine (Stanford University)

14:15 Particle Clustering in LArTPCs with Embedding Learning Convolutional Neural Networks - Dae Koh (SLAC)
Thursday 18 June 2020
Friday 19 June 2020
10:30 Lightning Talks
Lightning Talks
10:30 - 12:00
Contributions

10:30 Modeling Vector and Axial Nucleon Form Factors with Bayesian Neural Networks - Graczyk Krzysztof ( Institute of Theoretical Physics, Wroclaw University )

10:45 Clustering Cosmic Muon and Neutrino Interactions in MicroBooNE using a Deep Convolutional Neural Network - Joshua Mills (MicroBooNE)

11:00 Using a Convolutional Neural Network to Reconstruct Dead Channels in MicroBooNE - Katie Mason (Tufts University)

11:15 Learning from Data: the Zen of Deep Learning in KamLAND - Aobo Li (Boston University)

11:30 A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE LArTPC - Rui An (Illinois Institute of Techonology)
12:00

12:00 - 13:00
13:00 Lightning Talks
Lightning Talks
13:00 - 14:15
Contributions

13:00 Reconstructing 3D charge positions in LArTPCs using a CNN - Ralitsa Sharankova (Tufts University)

13:15 Efficient neutrino oscillation parameter inference using Gaussian processes - Nitish Nayak (University of California-Irvine)

13:30 Shower Reconstruction in Liquid Argon Time Projection Chambers using Graphical Neural Networks - Francois Drielsma (SLAC)

13:45 Interaction Clustering in Liquid Argon Time Projection Chamber using Graph Neural Networks - Qing Lin (SLAC)

14:00 Sparse Segmentation for Particle ID in ProtoDUNE - Carlos s Sarasty Segura (University of Cincinnati)
14:15 Closing
Closing
14:15 - 14:30
Contributions

14:15 Close out - 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)

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