Using Artificial Neural Networks to optimize scattering probabilities

Abstract: Monte Carlo event generators are used by theoretical particle physicists to get a better understanding of the phenomena in particle physics. Given the improvements in precision and accuracy of event generators, using these tools can be very CPU intensive. A method of unweighting events using artificial neural networks is presented to improve the efficiency of event generation. An introduction to machine learning as well as an introduction to the unweighting procedure is given as a basis. Results are given by comparing the “classical” and artificial neural network unweighting. The efficiency is expressed as factors of computing time for the matrix element and the model’s predicted value.