Abstract
The Matrix Element Method (MEM) is a powerful multivariate method allowing to maximally exploit the experimental and theoretical information available to an analysis. The method is reviewed in depth, and several recent applications of the MEM at LHC experiments are discussed, such as searches for rare processes and measurements of Standard Model observables in Higgs and Top physics. Finally, a new implementation of the MEM is presented. This project builds on established phase-space parametrisations known to greatly improve the speed of the calculations, and aims at a much improved modularity and maintainability compared to previous software, easing the use of the MEM for high-statistics data analyses.
Highlights
Multivariate techniques have become ever more ubiquitous in high energy physics, playing an important role at many levels, be it data collection, reconstruction or analysis
In the final steps of the latter, they allow isolating rare signals from backgrounds in searches for physics beyond the Standard Model (SM), searches for SM processes, or measurements of SM observables. Most of these techniques rely on Machine Learning (ML), whereby an algorithm is trained on a sample of data
Boosted Decision Trees (BDT) and Artificial Neural Networks (ANN), which fall into this category, are widely used and straightforward to implement in an analysis
Summary
Multivariate techniques have become ever more ubiquitous in high energy physics, playing an important role at many levels, be it data collection, reconstruction or analysis. In the final steps of the latter, they allow isolating rare signals from backgrounds in searches for physics beyond the Standard Model (SM), searches for SM processes, or measurements of SM observables. Most of these techniques rely on Machine Learning (ML), whereby an algorithm is trained on a sample of (usually simulated) data. The discrimination that can be attained by these means depends on the size of the samples used in the training phase In some cases, this can be a limiting factor and significantly restrict the reach of an analysis. The last section will be dedicated to issues related to the implementation of the method, the review of the MadWeight software, and the introduction of a new project: MoMEMta
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