Speaker
Description
The Deep Underground Neutrino Experiment (DUNE) is an international particle physics experiment looking to answer some of the largest unanswered questions in neutrino physics. DUNE uses a high-power neutrino beam produced at Fermi National Accelerator Laboratory (Fermilab) and consists of a near detector (ND), also located at Fermilab, and a far detector (FD) 1300 km away at the Sanford Underground Research Facility (SURF) in South Dakota. In the first phase of the experiment, the ND complex will include a modular liquid argon time projection chamber (ND-LAr) and a solid scintillator-based muon spectrometer (TMS), in addition to a beam-monitoring detector (SAND) and systems for moving ND-LAr and TMS away from the neutrino beam axis (PRISM). A prototype of ND-LAr, the 2x2 demonstrator, alongside a solid scintillator muon tagger provided by repurposed MINERvA planes, has been built and taken data at Fermilab.
For analyses with the ND, connecting particle tracks (such as muons) that exit the liquid argon active volume into the solid scintillator muon detector can improve particle identification and energy reconstruction, and alleviate pileup from the intense beam. To match tracks between detectors during reconstruction, we have explored using Graph Neural Networks (GNNs) to connect track segments between the liquid argon detector region and the solid scintillator detector planes. We have trained a GNN on reconstructed simulated data from the 2×2 demonstrator and repurposed MINERvA planes. We will evaluate its performance and then train a similar network on reconstructed ND-LAr and TMS simulations.