Abstract by Charles Lewis
Physics and Astronomy
Machine Learning Techniques to Approximate the Matrix Element Method for the \"ttH\" Process
Artificial Neural Networks (NN) serve as a way to approximate the parton-level probability distribution function of the Matrix Element Method (MEM) of multivariate analysis. The MEM is a powerful tool for analysis of the "ttH" process, a process where a top quark pair is produced in conjuction with a Higgs boson. However this method is slow and computationally expensive. Using a NN to approximate the parton-level function could dramatically speed up the analysis. We initially trained NN models with success for simplified 3 and 4 particle final state systems, using Python, Keras, and Tensorflow. Fitting a NN to the integrand for the full 8 particle final state system proved more difficult. We found that training on the log of the integrand and supplying additional derived parameters to the network produced a significantly improved accuracy that allowed us to fit the NN for the full "ttH process".