Speaker
Peter Loch
(University of Arizona (US))
Description
The basic signal of the ATLAS calorimeters are three-dimensional clusters of topologically connected cell signals formed by following signal significance patterns. These topo-clusters provide measures of their shape, location and signal character which are employed to apply a local hadronic calibration. The corresponding multi-dimensional calibration functions are determined by training neural networks to learn the basic topo-cluster response. Selected results from this approach are compared to the standard method using look-up tables. Significant improvements are found with respect to the signal linearity and resolution.
Authors
Jad Mathieu Sardain
(University of Arizona (US))
Kenneth Johns
(University of Arizona (US))
Olivia Quinn Pitcl
(University of Arizona (US))
Peter Loch
(University of Arizona (US))
