Speaker
Description
Liquid Argon Time Projection Chambers (LArTPCs) are a leading detector technology for precise 3D imaging and reconstruction of neutrino interactions, offering millimeter-scale spatial resolution. The SPINE (Scalable Particle Imaging with Neural Embeddings) reconstruction chain employs Sparse Convolutional Neural Networks for voxel-level feature extraction and Graph Neural Networks for particle-level clustering, forming a unified, hierarchical end-to-end pipeline for particle interaction reconstruction. In its current form, SPINE operates as a purely feed-forward pipeline, making downstream tasks such as instance clustering directly dependent on the accuracy of upstream outputs such as semantic segmentation — errors propagate without correction. This work introduces SPINE++, an iterative training strategy that couples semantic segmentation and instance clustering in a closed feedback loop, allowing each task to mutually refine the other across training iterations. We demonstrate that this approach yields significant improvements in reconstruction performance, achieving >85% reduction in segmentation errors, with the largest gains in challenging, high-density energy deposition areas.
| Contribution types | Long talk (30min + 10min Q/A) |
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