Speaker
Paul Glaysher
(DESY)
Description
Event classification trained on Monte Carlo data can lead to a training bias towards the generator of the training sample, typically evaluated as a systematic error by comparing to an alternative generator model.
For the case of the search for a top-quark pair produced in association with a Higgs boson decaying to bottom-quark at the LHC, we demonstrate how adversarial domain adaptation can reduce such training bias.
A signal vs background classification network is extended by a discriminator so that the classification response is more uniform for alternative background generators.
