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.

Search for the standard model Higgs boson produced in association with top quarks and decaying into a bb¯ pair in pp collisions at Aaboud1, Aad2, Abbott3 et al. 2018Phys. Rev. DSelf Cite

“…The integration is performed using VEGAS [88], following the implementation described in Ref. [89]. As in the reconstruction BDT, b-tagging information is used to reduce the number of jet-parton assignments considered in the calculation.…”

MadMiner: Machine Learning-Based Inference for Particle Physics

“…In this approximation, the likelihood becomes tractable. This category includes the Matrix Element Method [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26], Optimal Observables [27][28][29], and Shower and Event Deconstruction [30][31][32][33]. These methods make maximal use of the knowledge about the physics underlying the simulations.…”

Deployment of a Matrix Element Method code for the ttH channel analysis on GPU’s platform

“…This sophisticated method is however very CPU time consuming due to the investigation of all the main physical cases and requires huge powerful computing platforms to perform the CMS analyses carried out at our laboratory in a reasonable time. Thanks to the pioneering works of [9,10] on the implementation of the MEM on a single GPU, we were confident that the multi-GPU implementation was achievable.…”