Searching new physics with advanced data analysis techniques

Separating signal events from large Standard Model backgrounds is a highly challenging task at the Large Hadron Collider. The student will develop novel analysis techniques to perform this task. In recent years machine-learning methods have become increasingly important to exploit kinematic features in the separation between signal and background. Andrew will develop new techniques for unsupervised learning algorithms which allows to train these methods directly on data. He will then apply these methods to specific new physics models and assess the sensitivity in searching for such extensions of the Standard Model.