Name: Neutrino Physics and Machine Learning 2023
Start: 2023-08-22T06:00:00-04:00
End: 2023-08-25T12:40:00-04:00
Location: Tufts University

Neutrino Physics and Machine Learning 2023

from Tuesday, August 22, 2023 (6:00 AM) to Friday, August 25, 2023 (12:40 PM)

Monday, August 21, 2023
Tuesday, August 22, 2023
9:00 AM
9:00 AM - 12:15 PM
Contributions

9:00 AM Opening remark - Taritree Wongjirad (Tufts University)

9:15 AM Deep learning in voxelised neutrino detectors - Saul Alonso Monsalve (ETH Zurich)

10:00 AM Reconstructing Inelasticity in IceCube using Deep Neural Networks - Philip Weigel (Massachusetts Institute of Technology)

10:55 AM Accelerating event reconstruction in neutrino telescopes using sparse convolutional neural networks - Felix Yu (Harvard University)

11:30 AM End-to-End, Machine-Learning-Based Data Reconstruction Chain for the Short Baseline Neutrino Program - Francois Drielsma (SLAC)
1:40 PM
1:40 PM - 6:00 PM
Contributions

2:10 PM Deep Learning applications for electron neutrino reconstruction in the ICARUS experiment - Dae Koh (SLAC)

2:45 PM Particle Trajectory Reconstruction and Euclidian Equivariant Neural Networks - Omar Alterkait (Tufts University)

3:30 PM Identifying Particles and Neutrino Final States with Convolutional Neural Networks in MicroBooNE - Matthew Rosenberg (Tufts University)

4:15 PM NuGraph2: A Graph Neural Network for Event Reconstruction in Liquid Argon Time Projection Chambers - V Hewes (University of Cincinnati)
Wednesday, August 23, 2023
9:00 AM
9:00 AM - 12:55 PM
Contributions

9:15 AM Deep Learning for Water Cherenkov Detectors - Junjie Xia (IPMU)

10:00 AM Nova collaboration talk - Ashley Back (Indiana University)

11:00 AM Convolution Transformers for NOvA Event and Particle Classification - Alejandro Yankelevich (University of California, Irvine)

11:35 AM Differentiable simulation of the DUNE near detector liquid argon time projection chamber - Yifan Chen (SLAC)
1:30 PM
1:30 PM - 5:55 PM
Contributions

1:45 PM Implicit Neural Representation as a Differentiable Surrogate for Photon Propagation in a Monolithic Neutrino Detector - Patrick Tsang (SLAC)

2:20 PM Connecting the Dots: Using Multidetector Inputs in Machine Learning - Jessie Micallef (IAIFI at MIT and Tufts)

3:05 PM Translating Near to Far Detector with Deep Learning for DUNE-PRISM - Alex Wilkinson (University College London)

3:40 PM Uncertainty Quantification for DUNE ND Reconstruction - Dan Douglas (SLAC)

4:25 PM Diffusion-Based Generative Modeling for LArTPC Images - Zeviel Imani (Tufts University)

5:00 PM ML-Based Surrogate Modeling of Radiofrequency Quadrupole Accelerators - Joshua Villarreal (Massachusetts Institute of Technology)
Thursday, August 24, 2023
9:00 AM
9:00 AM - 12:10 PM
Contributions

9:00 AM nEXO's Machine Learning: Signal Analysis for Neutrinoless Double Beta Decay (remote) - Jason Brodsky (LLNL)

9:45 AM Variational Studies of nEXO's Topological Discriminator - Scott Schwartz (LLNL)

10:35 AM Search for solar neutrino and light dark matter with the PandaX-4T experiment - Wenbo Ma

11:20 AM Extracting the $\cos2\phi$ Asymmetry in the Drell-Yan process using Deep Neural Networks - Dinupa Nawarathne (New Mexico State University)
1:00 PM
1:00 PM - 6:00 PM
Contributions

1:00 PM An Introduction to Quantum Machine Learning for Neutrino Astronomy - Pavel Zhelnin (Harvard University) Carlos Argüelles-Delgado (Harvard University) Jeff Lazar (Harvard University)

1:45 PM Design, implementation and reliability of machine learning algorithms in JUNO - Leonard Imbert (Subatech, Nantes, France)

2:45 PM The RED-100 experiment (remote) - Dmitrii Rudik (MEPhI)

3:30 PM Pointlike events discrimination in the RED-100 experiment using ML algorithms (remote) - Olga Razuvaeva

4:20 PM HITMAN: Machine Learning Likelihoods for Arbitrary Optical Neutrino Detectors - Garrett Wendel (Penn State University) Douglas Cowen (Penn State)
Friday, August 25, 2023
9:00 AM
9:00 AM - 12:40 PM
Contributions

9:00 AM Towards Self-Supervised Learning for Neutrino Experiments (remote) - Radi Radev (CERN)

9:35 AM Machine Learning Applications for the neutrinoless double-beta decay search with LEGEND - Ann-Kathrin Schuetz (Lawrence Berkeley National Laboratory)

10:35 AM Faithful Pulse Shape Analysis for Germanium Detectors using Feature Importance Supervision - Katharina Kilgus (Universität Tübingen)

11:10 AM Machine Learning in KamLAND-Zen: An Old Detector Learning New ML Tricks - Hasung Song (Boston University)

11:55 AM MAJORANA demonstrator data release - Aobo Li (Boston University)

12:10 PM Closing - Kazuhiro Terao (SLAC)

Share this page

Direct link

Social networks

Calendaring