Title: Machine learning applications for the HIBEAM-NNBAR experiment at the European Spallation Source

Abstract: NNBAR is a proposed experiment for the European Spallation Source. The goal of the experiment is to observe the transformation $n-\Bar{n}$. Currently a cut based analysis is used to select signal events and discriminate against cosmic ray background. To further increase the signal efficiency machine learning was used. Most machine learning algorithms resulted in a higher signal efficiency at the cost of lowering the background rejection. However using the Linear Discriminant Analysis resulted in a new signal efficiency of 94% whilst having a predicted background rejection of roughly 100%. These results show that machine learning is a promising tool for increasing the signal efficiency at NNBAR.

Supervisor: David Milstead