A two-stage neural network that separates single and double resonant top production improves the expected 95% CL limit on the right-handed Wtb coupling f_R^V from 0.21 to 0.17.
General recipe to form input space for deep learning analysis of HEP scattering processes
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abstract
Deep learning neural network technique (DNN) is one of the most efficient and general approach of multivariate data analysis of the collider experiments. The important step of the analysis is the optimization of the input space for multivariate technique. In the article we propose the general recipe how to form the set of low-level observables sensitive for the differences in hard scattering processes at the colliders. It is shown in the paper that without any sophisticated analysis of the kinematic properties one can achieve close to optimal performance of DNN with the proposed general set of low-level observables.
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Separation of left-handed and anomalous right-handed vector operators contributions into the Wtb vertex for single and double resonant top quark production processes using a neural network
A two-stage neural network that separates single and double resonant top production improves the expected 95% CL limit on the right-handed Wtb coupling f_R^V from 0.21 to 0.17.