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June 22, 2020 to July 2, 2020
US/Central timezone

A deep neural network to direct the Pandora multi-algorithm LArTPC event reconstruction

Not scheduled
10m

Speaker

Dr Andrew Chappell (University of Warwick)

Description

The Deep Underground Neutrino Experiment (DUNE) is dedicated to addressing several key questions of particle physics and astrophysics: the preponderance of matter over antimatter, the dynamics of supernova neutrino bursts, and whether protons decay. DUNE’s liquid argon time-projection chambers for neutrino physics have created a need for new approaches to pattern recognition to fully exploit the high-resolution imaging offered by this technology. Identifying features in recorded events presents a significant challenge for automated algorithms. The Pandora Software Development Kit uses a multi-algorithm approach, in which individual algorithms each address a specific task in the reconstruction process. Here, we describe the details of a neural network performing semantic image segmentation to classify each hit according to its local event topology, and how such hit-level classification is used within the Pandora approach to direct the reconstruction algorithms.

Mini-abstract

A hybrid method using deep learning and algorithmic approaches to pattern recognition in DUNE

Experiment/Collaboration DUNE

Primary author

Dr Andrew Chappell (University of Warwick)

Presentation Materials