Speaker
Description
Reconstructing the type and energy of isolated pions from the ATLAS calorimeters is a key step in hadronic reconstruction. The existing methods were optimized early in the experiment lifetime. We recently showed that image-based deep learning can significantly improve the performance over these traditional techniques. This note presents an extension of that work using point cloud methods that do not require calorimeter clusters to be projected onto a fixed and regular grid. Instead, we use transformer, deep sets, and graph neural network architectures to process calorimeter clusters as point clouds. We demonstrate the performance of these new approaches as an important step towards a full deep learning-based low-level hadronic reconstruction.
| Career stage | Postdoc |
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