Speaker
Description
In 2017 the ATLAS experiment implemented an ensemble of neural networks
(NeuralRinger algorithm) dedicated to improving the performance of
filtering events containing electrons in the high-input rate online
environment of the Large Hadron Collider at CERN, Geneva. The ensemble
employs a concept of calorimetry rings. The training procedure and final
structure of the ensemble are used to minimize fluctuations from
detector response, according to the particle energy and position of
incidence. A detailed study was carried out to assess profile
distortions in crucial offline quantities through the usage of
statistical tests and residual analysis. These details and the online
performance of this algorithm during the 2017 data-taking will be
presented.
| Primary topic | Front-end readout and trigger |
|---|
