Speaker
Pedrame Bargassa
(LIP)
Description
The search for supersymmetric particles is one of the major goals of the Large Hadron Collider (LHC). Supersymmetric top (stop) searches play a very important role in this respect, but the unprecedented collision rate to be attained at the next high luminosity phase of the LHC poses new challenges for the separation between any new signal and the standard model background. In this talk, I show a novel application of the zoomed quantum annealing machine learning approach to classify the stop signal versus the background, and implement it in a quantum annealer machine. This approach together with the preprocessing of the data with principal component analysis may yield better results than conventional multivariate approaches.
Primary author
Pedrame Bargassa
(LIP)
