https://doi.org/10.1140/epjc/s10052-022-10665-7
Regular Article - Experimental Physics
End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks
1
Experimental Physics Department, CERN, Geneva, Switzerland
2
Manchester Metropolitan University, Manchester, UK
3
Florida State University, Tallahassee, USA
4
Massachusetts Institute of Technology, Cambridge, USA
5
Staffordshire University, Staffordshire, UK
Received:
14
April
2022
Accepted:
1
August
2022
Published online:
29
August
2022
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. To our knowledge, this work is the first-ever example of single-shot calorimetric reconstruction of 
particles in high-luminosity conditions with 200 pileup.
© The Author(s) 2022
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Funded by SCOAP3. SCOAP3 supports the goals of the International Year of Basic Sciences for Sustainable Development.
