Speaker
Description
Understanding hadron-argon interactions is essential for precise neutrino energy reconstruction and final-state interaction modeling in Liquid Argon Time Projection Chamber (LArTPC) experiments such as the Deep Underground Neutrino Experiment (DUNE), a long-baseline neutrino oscillation experiment that will use large LArTPC detectors to measure neutrino interactions and constrain systematic uncertainties. In particular, pion absorption and charge-exchange processes constitute significant sources of systematic uncertainty in neutrino oscillation measurements. ProtoDUNE Single-Phase (ProtoDUNE-SP), a large-scale LArTPC prototype operated at the CERN Neutrino Platform and exposed to charged-particle test beams in the few-GeV range, enables direct measurements of these processes.
This work focuses on the measurement of differential cross sections for pion absorption and charge exchange using the 2~GeV/$c$ pion test-beam dataset collected with ProtoDUNE-SP. A key component of this analysis is the identification of Michel electrons from $\pi \rightarrow \mu \rightarrow e$ decay chains, which helps separate different interaction topologies and improves background rejection. Michel electron identification will also assist in reliably calibrating the electromagnetic response in ProtoDUNE-SP and for the future DUNE detectors.
In this analysis, we apply NuGraph to identify Michel electrons. NuGraph is a graph neural network that models detector hits as nodes connected by spatial and temporal edges for particle and topology classification in LArTPC detectors. We first benchmark NuGraph's Michel electron classification performance using ICEBERG data, a small-scale LArTPC prototype used for DUNE electronics and reconstruction development, and then transfer the approach to ProtoDUNE-SP.
This poster presents the analysis strategy, the NuGraph-based classification studies, and discusses how these developments are expected to improve the pion cross-section measurement.