Publications (4)
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…
An investigation of fast simulation techniques for pion showers using kernel density estimators with the CALICE AHCAL Technological Prototype
CALICE Collaboration, A. Wilhahn, J. Utehs +29
In this article, the development and investigation of fast hadron shower simulation methods is presented. A test beam dataset has been recorded in 2018 at CERN with the AHCAL Techn…
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…
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…