Speaker
Description
The Deep Underground Neutrino Experiment (DUNE) will operate four large-scale Liquid-Argon Time-Projection Chambers (LArTPCs) at the far site in South Dakota, producing high-resolution images of neutrino interactions.
LArTPCs represent a step-change in neutrino interaction imaging and the resultant images can be highly detailed and complex. Extracting the maximum value from LArTPC hardware requires correspondingly sophisticated pattern-recognition software to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex, which is non-trivial due to the interaction occurring at any point within the detector volume. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle.
A new vertex-finding procedure presented in this talk integrates a U-Net performing hit-level classification into the multi-algorithm approach used by the Pandora pattern recognition framework to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of traditional pattern-recognition algorithms incorporating knowledge of the detector, demonstrating that traditional and machine learning methods need not be mutually exclusive in leveraging the potential of machine learning for neutrino physics.
| Consider for long presentation | No |
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