Accelerated Matrix Element Method with Parallel Computing

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.

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Cited by in corpus (12)

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• Constraining CP-violation in the Higgs-top-quark interaction using machine-learning-based inference
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• Effective LHC measurements with matrix elements and machine learning
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• Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider
• Many-gluon tree amplitudes on modern GPUs: A case study for novel event generators
• Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
• Application of the matrix element method to Higgs boson pair production in the channel $\text{HH}\to \text{b}\bar{\text{b}}\text{W}{\text{W}}^{\ast }$ at the LHC