In searching for supersymmetry and other new physics at the Large Hadron Collider one of the most important ingredients is accurate knowledge of the cross section for supersymmetric particle production. In quantum field theory this can be calculated as a series expansion in the coupling (of the strong force), where complications in evaluating the terms grow unreasonably beyond the first term. Results are know for the second term, however, in the form of computationally challenging integrals that must be re-evaluated for every value of the parameters of the new physics model. Typically, an evaluation of all relevant cross sections for a given parameter point would take on scale of hours. This makes investigations of the supersymmetric parameter space brutal in terms of the necessary computing power.
We have an ongoing long term project where we use modern computational methods and methods from machine learning to speed up high precision calculations on superclusters. The goal is to allow fast evaluation of the cross sections -- typically within one second -- and a reliable estimate of the associated error in the regression. In the past we have seen great success using boosted decision trees and Gaussian processes.
We can offer a number of master thesis projects covering all sorts of tasks from higher order quantum field theory calculations of cross sections to improve precision, to pure machine learning projects performing fast regression on existing cross section samples, and anything in between.