Speaker
Description
The Deep Underground Neutrino Experiment (DUNE) is a next-generation experiment designed to explore fundamental neutrino physics using large-scale Liquid Argon Time Projection Chambers (LArTPCs). To fully exploit the detector's capabilities, the Wire-Cell event reconstruction framework has been developed by utilizing multi-plane 2D projections and precise wire geometry. In this work, we present ongoing Wire-Cell reconstruction efforts in ProtoDUNE, highlighting advancements in signal processing and imaging. First, we implement a Deep Neural Network (DNN) architecture for resource-efficient 2D signal processing while preserving its performance. Second, as 3D imaging is a core Wire-Cell process that reconstructs signals in 3D space and overcomes wire geometry ambiguities through algorithms such as de-ghosting, we quantitatively evaluate its reconstruction performance. Together, these developments enhance computational efficiency in signal processing and validate the 3D imaging capabilities critical for future DUNE's physics program.