Neutrino Physics and Machine Learning 2023

Session

Session 1

22 Aug 2023, 09:00

Tufts University

31. Opening remark

Taritree Wongjirad (Tufts University)

22/08/2023, 09:00

1. Deep learning in voxelised neutrino detectors

Dr Saul Alonso Monsalve (ETH Zurich)

22/08/2023, 09:15

Individual Talk

Deep learning methods are becoming key in the data analysis of particle physics experiments. One clear example is the improvement of neutrino detection using neural networks. Current neutrino experiments are leveraging these techniques, which, in combination, have exhibited to outperform standard tools in several domains, such as identifying neutrino interactions or reconstructing the...

28. Reconstructing Inelasticity in IceCube using Deep Neural Networks

Philip Weigel (Massachusetts Institute of Technology)

22/08/2023, 10:00

Collaboration Talk

The IceCube neutrino observatory is a gigaton-scale water Cherenkov detector located at the South Pole instrumented with 5160 optical modules in a cubic kilometer of ice. When a high energy neutrino undergoes deep inelastic scattering, the inelasticity of the interaction is the fraction of energy deposited in the hadronic shower to the incoming neutrino energy. For a muon neutrino event, where...

7. Accelerating event reconstruction in neutrino telescopes using sparse convolutional neural networks

Felix Yu (Harvard University)

22/08/2023, 10:55

Individual Talk

Convolutional neural networks (CNNs) have seen extensive applications in scientific data analysis, including in neutrino telescopes. However, the data from these experiments present numerous challenges to CNNs, such as non-regular geometry, sparsity, and high dimensionality. In this talk, I will present sparse submanifold convolutions (SSCNNs) as a solution to these issues and show that the...

34. End-to-End, Machine-Learning-Based Data Reconstruction Chain for the Short Baseline Neutrino Program

Francois Drielsma (SLAC)

22/08/2023, 11:30

Recent leaps in Computer Vision (CV), made possible by Machine Learning (ML), have motivated a new approach to the analysis of particle imaging detector data. Unlike previous efforts which tackled isolated CV tasks, this talk introduces an end-to-end, ML-based data reconstruction chain for Liquid Argon Time Projection Chambers (LArTPCs), the state-of-the-art in precision imaging at the...

3. Applying Machine Learning to vertex recognition for neutrino interactions

Mr Oscar Moreno Palacios (William & Mary)

Collaboration Talk

The MINERvA experiment studies neutrinos cross sections with different nuclei. Neutrino vertex
recognition plays a key role in reconstructing neutrino interactions. This research aims to enhance
previous Machine Learning neutrino vertex recognition models produced in MINERvA using Deep
Convolutional Neural Networks (DCNN). The approach focuses on extending neutrino interaction
image...