Speaker
Description
Wire-Cell is a mature, production-level LArTPC reconstruction framework that integrates physics-driven models with targeted machine learning to perform signal processing and 3D tomographic reconstruction. Current reconstruction approaches in Wire-Cell primarily exploit geometry, charge, and local connectivity, while higher-level topological information remains only partially encoded. In addition, reconstruction performance suffers from persistent simulation-to-data discrepancies arising from relying on Monte Carlo samples for development and training. Building on previous ML integrations, a Wire-Cell Foundation Model is being developed to learn rich topological and contextual representations directly from sparse detector data in a self-supervised manner. This model aims to encode global structure beyond local connectivity, while naturally mitigating the simulation-to-data gap. The resulting representations will provide a reusable substrate for downstream tasks including deghosting, clustering, vertexing, and particle-flow reconstruction. This talk will discuss the preparation of LArTPC data for self-supervised training, the model architecture, the initial training procedure, and preliminary studies of the learned representations and reconstruction performance.
| Contribution types | Short talk (15min + 5min Q/A) |
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