Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5493277 | Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment | 2016 | 24 Pages |
Abstract
In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new-physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery-significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications.
Related Topics
Physical Sciences and Engineering
Physics and Astronomy
Instrumentation
Authors
M.Ã. Sahin, D. Krücker, I.-A. Melzer-Pellmann,