Speaker
Description
Liquid argon time projection chambers (LArTPCs) 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 using MicroBooNE open datasets and explore machine-learning-based approaches for its identification. While machine learning techniques have been widely applied to particle reconstruction, neutron-induced 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-induced activity and contribute to a more complete reconstruction of neutrino interactions. We present the development and implementation of these methods, along with preliminary studies of their performance. Thank you very much for your help.
These approaches aim to improve the identification of neutron-induced activity and contribute to a more complete reconstruction of neutrino interactions. We present 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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