Speaker
Description
Liquid argon time projection chambers (LArTPCs), such as those that will be used in the Deep Underground Neutrino Experiment (DUNE), provide high-resolution, three-dimensional imaging of neutrino interactions. A persistent challenge in these detectors is neutron reconstruction, as neutrons do not produce direct ionization tracks and are instead inferred through secondary interactions. This leads to incomplete event reconstruction and limits the precision of neutrino energy measurements.
In this work, we study neutron-induced activity in a prototype near detector system for DUNE, the 2×2 demonstrator, and explore machine learning–based approaches for its identification. While machine learning techniques have been widely applied to particle reconstruction, neutron-related activity remains relatively unexplored due to its indirect and diffuse signatures. We investigate models that combine local features with the global structure of the interaction to learn correlations across the event.
These approaches aim to improve the identification of neutron-related activity and contribute to more complete reconstruction of neutrino interactions. In this work, we review the current status of the development and implementation of these methods, along with preliminary studies of their performance.
| Contribution types | Short talk (15min + 5min Q/A) |
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