Self-Organizing Maps in High Energy Physics

Abstract

The Self-Organizing-Map (SOM) is a widely used neural network for dimensional reduction and clustering. It has yet to find its use in high energy physics. This paper discusses two applications of SOM: first, we find map regions with a high relative content of a rare process (H → WW*). Second we obtain Monte Carlo normalization factors for different physics processes by fitting the dimensionally reduced representation. Analysis and training are performed on ATLAS open data.