27 citations · 80 across the 7 of their papers we have counts for
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physics.ins-det2024
Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning
S. Lai, J. Utehs, A. Wilhahn +48
To achieve state-of-the-art jet energy resolution for Particle Flow, sophisticated energy clustering algorithms must be developed that can fully exploit available information to se…
physics.ins-det2024★ 3 cited
Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter
M. Aamir, G. Adamov, T. Adams +568
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 transv…
physics.ins-det2024
Software Compensation for Highly Granular Calorimeters using Machine Learning
S. Lai, J. Utehs, A. Wilhahn +61
A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event in…