Orateur
Description
Upgrade II constitutes a major leap forward at LHCb towards more and better data, driven by new hardware and software that will enable an ambitious increase of the current instant luminosity, thus, the experiment's physics reach. In particular, the calorimeter will be replaced by the new branded PicoCal, featuring better granularity and timing capabilities.
A significant challenge will be to keep or even improve accuracy with a much busier and granular detector, facing a huge increase of the data volumes to process real-time (30 MHz of inelastic collisions rate at LHCb). Studies by various groups among different institutes and experiments have demonstrated that GNNs can reach much better resolution than traditional clustering methods [1-4]. An important effort has been placed in making these architectures light enough so that they can run within the computing budget of the extreme data flow environments of real-time data processing in HEP experiments.
This presentation will show the preliminary results within the ODISSEE project to implement PicoCal GNN reconstruction in the Allen, LHCb's GPU High-Level-Trigger framework, and discuss the different GNN architectures that we are exploring to achieve the most performing results, looking forward to exchanging ideas with the comunity.
[1] Learning representations of irregular particle-detector geometry with distance-weighted graph networks
[2] Towards lightweight and distilled graph neural networks for efficient particle reconstruction in LHCb's next-generation calorimeter
[3] Graph Neural Network–based Energy Reconstruction for the LHCb Upgrade II Electromagnetic Calorimeter
4: Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics