Machine learning-based reconstruction in pixelated liquid argon time projection chambers

Orgho Neogi; University of Iowa

The Deep Underground Neutrino Experiment (DUNE) will address open issues in neutrino physics such as the measurement of the CP-violating phase in neutrino oscillations and the neutrino mass ordering. The 2x2 demonstrator is a single-phase liquid argon time projection chamber (LArTPC), with four modules, operated as a prototype for the DUNE Liquid Argon Near Detector (ND-LAr). Based on the ArgonCube design concept, the 2x2 features a novel pixelated charge readout and advanced high-coverage photon detection system. Machine learning (specifically the lartpc_mlreco3d package) can be used to form a complete reconstruction pipeline of the 2x2 events. This talk will describe the workings on this reconstruction and its current performance.