Applications of machine learning to Higgs physics

Project Description:

The Higgs boson is central to our understanding of the Standard Model. This project is focussed on the study of this particle and in particular the search for for H decays into pairs of b quarks using data from the LHC experiment at ATLAS. This project will also involve exploring the application of modern machine learning algorithms to this problem; with the aim to improve our understanding of fundamental physics. Machine learning areas of interest include Neural Networks, Decision Trees, Support Vector Machines, Deep Learning and generalisation.

Requirements:

There are opportunities to spend one year in Geneva during the course of their PhD, while this is not mandatory it is strongly encouraged. Some grasp of C++ and statistics is desirable.

SPA Academics: Adrian Bevan, Seth Zenz