Speaker
Kai Habermann
(University of Bonn)
Description
The Self-Organizing-Map (SOM) is a widely used neural
net for data analysis, dimension reduction and
clustering. It has yet to find use in high energy
particle physics. This paper discusses two
applications of SOM in particle physics. First, we were
able to obtain high separation of rare processes in
regions of the dimensionally reduced representation.
Second, we obtained Monte Carlo scale factors by fitting
the dimensionally reduced representation.
Analysis and training were performed on the data of the
ATLAS Machine Learning challenge and on Open
Data.
Significance
First application of SOM to searches in particle physics.
| Speaker time zone | Compatible with Europe |
|---|
Author
Kai Habermann
(University of Bonn)
Co-author
Dr
Eckhard von Toerne
(University of Bonn)
