Name: Neutrino Physics and Machine Learning (NPML)
Start: 2020-07-10T10:00:00-05:00
End: 2020-07-24T17:00:00-05:00
Location: No location set

Neutrino Physics and Machine Learning (NPML)

from Friday, July 10, 2020 (10:00 AM) to Friday, July 24, 2020 (5:00 PM)

Monday, July 6, 2020
Tuesday, July 7, 2020
Wednesday, July 8, 2020
Thursday, July 9, 2020
Friday, July 10, 2020
10:00 AM
10:00 AM - 12:15 PM
Contributions

10:00 AM 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)

10:10 AM Welcome: High Energy Physics and Machine Learning - Michael Cooke (U.S. Department of Energy)

10:25 AM White paper introduction - Adam Aurisano (University of Cincinnati)

10:45 AM DL In MicroBooNE - Taritree Wongjirad (Tufts University)

11:25 AM Using Sparse Convolutional Neural Networks in MicroBooNE - Ran Itay (WEIZMANN INST.)

11:50 AM A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE LArTPC - Rui An (Illinois Institute of Techonology)
1:00 PM
1:00 PM - 3:35 PM
Contributions

1:00 PM ML challenges in Theia and WBLS - Björn Wonsak (University of Hamburg)

1:40 PM Scalable, End-to-End Deep Learning Based Data Reconstruction Chain for 3D Particle Imaging Detectors - Francois Drielsma (SLAC)

2:20 PM Scalable 3D Semantic Segmentation and Point Proposal Network for large-scale high resolution particle imaging detectors - Laura Domine (Stanford University)

2:45 PM Proposal-free Deep Sparse Convolutional Neural Network for 3D Pixel Clustering - Dae Koh (SLAC)

3:10 PM Enabling A Deep Neural Networks based 3D LArTPC Data Reconstruction Chain for ICARUS - Patrick Tsang (SLAC)
Saturday, July 11, 2020
Sunday, July 12, 2020