Using Graph Neural Networks to Reconstruct Charged Pion Showers in the CMS High Granularity Calorimeter
Journal of Instrumentation 2024
CMS Collaboration, Arnis Lektauers

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.


Keywords
Calorimeters | Pattern recognition, cluster finding, calibration and fitting methods | Performance of High Energy Physics Detectors | Si microstrip and pad detectors
DOI
10.1088/1748-0221/19/11/P11025
Hyperlink
https://iopscience.iop.org/article/10.1088/1748-0221/19/11/P11025

The CMS Collaboration, Lektauers, A.... [et al.] Using Graph Neural Networks to Reconstruct Charged Pion Showers in the CMS High Granularity Calorimeter. Journal of Instrumentation, 2024, Vol. 19, No. 11, Article number P11025. ISSN 1748-0221. Available from: doi:10.1088/1748-0221/19/11/P11025

Publication language
English (en)
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