End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks
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10052_2022_Article_10665.pdf
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Author(s)
Qasim, Shah R.
Chernyavskaya, Nadezda
Kieseler, Jan
Long, Kenneth
Viazlo, Oleksandr
Pierini, Maurizio
Nawaz, Raheel
Date Issued
August 29, 2022
Publisher
Springer Berlin Heidelberg
Citation
The European Physical Journal C. 2022 Aug 29;82(8):753
Version
Final published version
Abstract
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. To our knowledge, this work is the first-ever example of single-shot calorimetric reconstruction of
$${\mathcal {O}}(1000)$$
O
(
1000
)
particles in high-luminosity conditions with 200 pileup.
MIT Department
Massachusetts Institute of Technology. Department of Physics
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1140/epjc/s10052-022-10665-7
