Abstract
The ATLAS experiment implemented an ensemble of neural networks (Neural-Ringer algorithm) dedicated to improving the performance of event filters selecting electrons in the high-input-rate online environment of the Large Hadron Collider (LHC) at CERN. This algorithm has been used online since 2017 to select electrons with transverse energies (ET) above 15 GeV. By taking advantage of calorimetry knowledge, the ensemble employs ring energy sums concentric to the electron candidate energy barycenter. The training procedure and final structure of the ensemble are designed to keep detector response flat with respect to particle energy and position. A detailed study was carried out to assess possible profile distortions in crucial offline quantities through the usage of statistical tests and residual analysis. NeuralRinger operation maintained high electron efficiency while improving fake rejection by a factor of 2 to 3, with negligible residuals in the offline quantities.
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