End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks

• URL: http://arxiv.org/abs/2204.01681v1
• Date: Mon, 4 Apr 2022 17:51:43 GMT
• Title: End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks
• Authors: Shah Rukh Qasim, Nadezda Chernyavskaya, Jan Kieseler, Kenneth Long, Oleksandr Viazlo, Maurizio Pierini, and Raheel Nawaz
• Abstract summary: We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in granular calorimeters. The algorithm exploits a distance-weighted graph neural network, trained with object condensation, a graph segmentation technique. This work is the first-ever example of single-shot calorimetric reconstruction of $cal O(1000)$ particles in high-luminosity conditions with 200 pileup to our knowledge.
• Score: 18.347013421412793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a distance-weighted graph neural network, trained with object condensation, a graph segmentation technique. Through a single-shot approach, the reconstruction task is paired with energy regression. We describe the reconstruction performance in terms of efficiency as well as in terms of energy resolution. In addition, we show the jet reconstruction performance of our method and discuss its inference computational cost. This work is the first-ever example of single-shot calorimetric reconstruction of ${\cal O}(1000)$ particles in high-luminosity conditions with 200 pileup to our knowledge.

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