Accelerated Matrix Element Method with Parallel Computing

TL;DR

This paper presents a GPU-accelerated implementation of the matrix element method, significantly reducing computation time and enhancing its practicality for complex particle physics analyses at collider experiments.

Contribution

The paper introduces a parallel computing approach using GPUs to accelerate the matrix element method, making it feasible for complex, high-dimensional problems in collider physics.

Findings

Achieved substantial speedups with GPU implementation

Enabled practical use of matrix element method for complex final states

Demonstrated parallelization benefits for multidimensional integration

Abstract

The matrix element method utilizes ab initio calculations of probability densities as powerful discriminants for processes of interest in experimental particle physics. The method has already been used successfully at previous and current collider experiments. However, the computational complexity of this method for final states with many particles and degrees of freedom sets it at a disadvantage compared to supervised classification methods such as decision trees, k nearest-neighbour, or neural networks. This note presents a concrete implementation of the matrix element technique using graphics processing units. Due to the intrinsic parallelizability of multidimensional integration, dramatic speedups can be readily achieved, which makes the matrix element technique viable for general usage at collider experiments.