Performance and optimization of support vector machines in high-energy physics classification problems

TL;DR

This paper advocates for using Support Vector Machines in high-energy physics searches, demonstrating their effectiveness and introducing an automated hyper-parameter optimization method and a new C++ interface.

Contribution

It introduces an automated discovery-significance based hyper-parameter optimization for SVMs and a new C++ LIBSVM interface called SVM-HINT for physics applications.

Findings

SVMs are effective for high-energy physics classification tasks.

Automated hyper-parameter optimization improves SVM performance.

SVM-HINT is a new tool available on Github.

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. A new C++ LIBSVM interface called SVM-HINT is developed and available on Github.