3 citations · 3 across the 1 of their papers we have counts for
2 papers
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…
physics.ins-det2022★ 3 cited
Machine-learning-based prediction of parameters of secondaries in hadronic showers using calorimetric observables
M. Chadeeva, S. Korpachev
The paper describes a novel neural-network-based approach to study the distributions of secondaries produced in hadronic showers using observables provided by highly granular calor…