Speaker
Lorenz Vogel
(ITP, Heidelberg University)
Description
ATLAS explores modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of topologically connected cells (topo-clusters). The Bayesian neural network (BNN) approach yields a continuous and smooth calibration function, including uncertainties on the calibrated energy per topo-cluster. In this talk the performance of this BNN-derived calibration is compared to an earlier calibration network and standard table-lookup-based calibrations. The BNN uncertainties are confirmed using repulsive ensembles and validated through the pull distributions. First results indicate that unexpectedly large learned uncertainties can be linked to particular detector regions.
Authors
Barry Dillon
(Ulster University)
Luigi Favaro
(Universite Catholique de Louvain (UCL) (BE))
Tilman Plehn
Lorenz Vogel
(ITP, Heidelberg University)
Sangwoong Yoon
(University College London (UCL))
