Speaker
Description
The Liquid Argon Time Projection Chamber (LArTPC) technology provides high-resolution spatial and calorimetric information which can lead to great capabilities for particle identification. MicroBooNE was one of the first large LArTPC experiments to operate in a neutrino beam. This talk presents the integration of NuGraph2, a reconstruction tool based on machine learning (ML), into MicroBooNE’s reconstruction and analysis workflow. We will discuss the effort in deploying this ML-based reconstruction tool on real LArTPC neutrino data, including strategies for minimizing simulation-induced bias and assessments of robustness against detector systematics, as well as its impact on reconstruction performance and downstream physics analyses.
NuGraph2 is a graph neural network developed for neutrino event reconstruction. The network makes hit-level predictions, including a cosmic vs. neutrino interaction classifier and a semantic decoder which labels the hits according to their particle types. In MicroBooNE, we have integrated NuGraph2 with Pandora to form a combined reconstruction framework with significantly improved performance. The cosmic filter allows us to better reject cosmic rays, while the semantic labels are utilized for better particle identification. These reconstruction-level gains also translate to enhancement in physics analyses. This includes cases that were not explicitly targeted during the network training, such as Beyond the Standard Model (BSM) models with e+e- final states.
| Contribution types | Standard talk (20min + 5min Q/A) |
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