Neutrino Physics and Machine Learning 2024

Session

Day 3 - Morning

Jun 27, 2024, 9:00 AM

HCI J4 (ETH Zurich)

Description

Machine learning based data reconstruction and analysis at the DUNE far detector

24. A novel approach in the Pandora multi-algorithm reconstruction to tackle challenging topologies at DUNE

Maria Brigida Brunetti (University of Warwick)

6/27/24, 9:00 AM

The Deep Underground Neutrino Experiment (DUNE) will measure all long-baseline neutrino oscillation parameters, including the CP-violating phase and the neutrino mass ordering, in one single experiment. DUNE will also study astrophysical neutrinos and perform a broad range of new physics searches. This ambitious programme is enabled by the very high-resolution imaging capabilities of...

30. Machine Learning Approaches to Particle Identification in the DUNE Far Detector

Isobel Mawby (Lancaster University)

6/27/24, 9:25 AM

One of the primary oscillation physics goals of the Deep Underground Neutrino Experiment (DUNE) far detector (FD) is the measurement of CP violation in the neutrino sector. To achieve this, DUNE plans to employ large-scale liquid-argon time-projection chamber technology to capture neutrino interactions in unprecedented detail. Such fine-grain images demand a highly sophisticated automated...

9. NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Giuseppe Cerati (Fermilab)

6/27/24, 9:50 AM

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore...

28. NuGraph3: Towards Full LArTPC Reconstruction using GNNs

Adam Aurisano (University of Cincinnati)

6/27/24, 11:00 AM

The highly detailed images produced by liquid argon time projection chamber (LArTPC) technology hold the promise of an unprecedented window into neutrino interactions; however, traditional reconstruction techniques struggle to efficiently use all available information. This is especially true for complicated interactions produced by tau neutrinos, which are typically large, consist of many...

7. Vertex-finding in a DUNE far-detector using Pandora deep-learning

Andrew Chappell (University of Warwick)

6/27/24, 11:25 AM

The Deep Underground Neutrino Experiment will operate large-scale Liquid-Argon Time-Projection Chambers at the far site in South Dakota, producing high-resolution images of neutrino interactions. Extracting the maximum value from the images requires sophisticated pattern-recognition to interpret detector signals as physically meaningful objects for physics analyses. Identifying the neutrino...

36. Enhancing Liquid Argon TPCs Performance in Low-Energy Physics Classification Problems with Quantum Machine Learning

Roberto Moretti (INFN - Sezione di Milano Bicocca)

6/27/24, 11:50 AM

Deep Learning (DL) techniques for background event rejection in Liquid Argon Time Projection Chambers (LArTPCs) have been extensively studied for various physics channels [1,2], yielding promising results. However, the potential of massive LArTPCs in the low-energy regime remains to be fully exploited, particularly in the classification of few-hits events that encode information hardly...