Point Cloud Deep Learning Methods for Pion Reconstruction in the ATLAS Detector

The reconstruction and calibration of hadronic final states in the ATLAS detector present complex experimental challenges. For isolated pions, in particular, classifying 𝜋0 versus 𝜋± and calibrating pion energy deposits in the ATLAS calorimeters are key steps in the hadronic reconstruction process. The baseline methods for local hadronic calibration were optimized early in the lifetime of the ATLAS experiment. Recently, image-based deep learning techniques demonstrated significant improvements in the performance over these traditional techniques. We present an extension of that work using point cloud methods that do not require calorimeter clusters or particle tracks to be projected onto a fixed and regular grid. Instead, transformer, deep sets, and graph neural network architectures are used to process calorimeter clusters and particle tracks as point clouds. The performance of these new approaches is an important step towards a fully deep learning-based low-level hadronic reconstruction.