Sri Tata

srivatsatata@yahoo.com

Caltech, Travel dates:

Project: Discovering the top quark at the LHC with unsupervised Machine Learning algorithms

Supervisors: Maurizio Pierini, Jean-Roch Vlimant

The study of top quark at the LHC makes use of so-called supervised machine learning (ML) algorithms (e.g., the Neural Network used to train the jet b tagging). Unlike other particles decaying to jets (e.g. W, Z, or H), the top quark is abundantly produced at the LHC. The large signal-to-background ratio makes it an ideal case to test the discovery potential of the LHC. We propose to define a search for top quarks in 1l+jets events, based on unsupervised ML algorithms. Without teaching the algorithm what a top quark is and what it looks like, we propose to test the possibility of highlighting the existence of the top quark by clustering similar events into classes, that could then be compared to the known background. The top signal should emerge as an unassociated event category. This would be the first application of unsupervised ML algorithms at the LHC.