Orateur
Description
SPINE (Scalable Particle Imaging with Neural Embeddings) is a transformative, ML-native approach to reconstruction and is rapidly becoming a common reconstruction pipeline across LArTPC experiments. Building on its strong performance in ICARUS, SBND and ND-LAr, we are extending SPINE to the DUNE Far Detector and its two large-scale prototypes at CERN. This is not a direct port of existing configurations. The scale, readout geometries and operating conditions of these detectors call for substantial development at every level of the chain, from data preparation and training infrastructure to network design and the evaluation of reconstruction performance.
Using sparse CNNs and GNNs, SPINE performs end-to-end event reconstruction in one coherent framework, combining semantic segmentation, vertexing, particle clustering and kinematic reconstruction. We are adapting and improving each of these stages for the ProtoDUNE detectors and the Far Detector. We evaluate their performance with calibration-level benchmarks such as Michel electron tagging, electromagnetic shower reconstruction and dE/dx resolution across detector configurations. We are also developing a more efficient staged training workflow.
We also study cross-detector generalization and domain adaptation, which are essential for deploying SPINE at scale throughout the DUNE program.
Together, these developments move SPINE beyond its original deployments toward a scalable, experiment-agnostic reconstruction framework that can meet the demands of next-generation neutrino physics.