Speaker
José Manuel Clavijo Columbié
(DESY)
Description
The ATLAS ttH(bb) analysis is limited by the background modelling uncertainties, that result in a bias of the classifier towards the Monte Carlo generator used for training. We apply adversarial domain adaptation to train a more generic classifier. We employ a neural network that simultaneously classifies the signal versus background events, while minimizing the difference of the classifier response to two alternative background MC models.
Primary author
José Manuel Clavijo Columbié
(DESY)
