Jul 6 – 13, 2022
Bologna, Italy
Europe/Rome timezone

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

Jul 8, 2022, 7:05 PM
1h 25m
Bologna, Italy

Bologna, Italy

Palazzo della Cultura e dei Congressi
Poster Operation, Performance and Upgrade (Incl. HL-LHC) of Present Detectors Poster Session


Dilia Maria Portillo Quintero (TRIUMF (CA))


Reconstructing the type and energy of isolated pions from the ATLAS calorimeters is a key step in the hadronic reconstruction. 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 over the performance over these traditional techniques. This poster presents 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. This note demonstrates the performance of these new approaches as an important step towards a full deep learning-based low-level hadronic reconstruction.

In-person participation Yes

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