# MoMEMta, a modular toolkit for the Matrix Element Method at the LHC

@article{Brochet2018MoMEMtaAM,
title={MoMEMta, a modular toolkit for the Matrix Element Method at the LHC},
author={S{\'e}bastien Brochet and C. Delaere and Brieuc François and Vincent Lema{\^i}tre and Alexandre Mertens and Alessia Saggio and M Vidal Marono and S{\'e}bastien Wertz},
journal={The European Physical Journal C},
year={2018},
volume={79},
pages={1-12}
}
• Published 22 May 2018
• Physics
• The European Physical Journal C
The Matrix Element Method has proven to be a powerful method to optimally exploit the information available in detector data. Its widespread use is nevertheless impeded by its complexity and the associated computing time. MoMEMta, a C++ software package to compute the integrals at the core of the method, provides a versatile implementation of the Matrix Element Method to both the theory and experiment communities. Its modular structure covers the needs of experimental analysis workflows at the…
10 Citations

### Advanced multivariate analysis methods for use by the experiments at the Large Hadron Collider

In the course of the past four years, AMVA4NewPhysics, a Horizon2020-funded Marie Skłodowska-Curie (MSCA) Innovative Training Network, focused on the study of Multivariate Analysis Methods and

### Advanced Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider

• Physics
ArXiv
• 2021
The most relevant new tools, among those studied and developed, are presented along with the evaluation of their performances and promise to further improve the precision of measurements of fundamental physics parameters and the reach of searches for new phenomena.

### Deep Learning for the Matrix Element Method

• Computer Science
Proceedings of 41st International Conference on High Energy physics — PoS(ICHEP2022)
• 2022
An application of deep learning is described that dramatically speeds-up matrix element (ME) method calculations and novel cyberinfrastructure developed to execute ME-based analyses on heterogeneous computing platforms.

### Matrix element regression with deep neural networks — Breaking the CPU barrier

• Computer Science
• 2020
This paper investigates the use of a Deep Neural Network built by regression of the MEM integral as an ansatz for analysis, especially in the search for new physics.

### Machine Learning in High Energy Physics Community White Paper

• Physics, Education
Journal of Physics: Conference Series
• 2018
Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications

### Resolving Combinatorial Ambiguities in Dilepton $t \bar t$ Event Topologies with Neural Networks

• Physics
• 2022
We study the potential of deep learning to resolve the combinatorial problem in SUSY-like events with two invisible particles at the LHC. As a concrete example, we focus on dileptonic t ¯ t events,

### Fermionic dark matter: from models to collider searches

In this thesis, we investigate from diverse point of views, the dark matter problem. First, we study the doublet-triplet fermion model, a simple extension of the Standard Model with an extra Z2

### Search for the production of Higgs bosons in association with top quarks and decaying into bottom quark pairs with the ATLAS detector

The Standard Model of particle physics (SM) describes mass generation of fundamental particles via the Brout-Englert-Higgs mechanism. It predicts Yukawa interactions between the Higgs boson and

• 2019

## References

SHOWING 1-10 OF 71 REFERENCES

### Automation of the matrix element reweighting method

• Physics, Computer Science
• 2010
This work presents a procedure that allows to automatically evaluate the weights for any process of interest in the standard model and beyond, and creates a phase-space mapping designed to efficiently perform the integration of the squared matrix element and the transfer functions.

### DELPHES 3: A modular framework for fast-simulation of generic collider experiments

• Physics
• 2014
The new version of the DELPHES C++ fast-simulation framework is presented. The tool is written in C++ and is interfaced with the most common Monte Carlo file formats (LHEF, HepMC, STDHEP). Its

### LHAPDF6: parton density access in the LHC precision era

• Physics
• 2015
The Fortran LHAPDF library has been a long-term workhorse in particle physics, providing standardised access to parton density functions for experimental and phenomenological purposes alike,

### The Matrix Element Method and QCD Radiation

• Physics
• 2011
The matrix element method (MEM) has been extensively used for the analysis of top-quark and W-boson physics at the Tevatron, but in general without dedicated treatment of initial state QCD radiation.

### Unravelling tth via the matrix element method.

• Physics
Physical review letters
• 2013
It is found that a moderate integrated luminosity in the next LHC run will be enough to make the signature involving both W's decaying leptonically as sensitive as the single-lepton one.

### Event generation with SHERPA 1.1

• Physics
• 2008
In this paper the current release of the Monte Carlo event generator Sherpa, version 1.1, is presented. Sherpa is a general-purpose tool for the simulation of particle collisions at high-energy

### Event generation with

• Physics
• 2009
In this paper the current release of the Monte Carlo event generator Sherpa, version 1.1, is presented. Sherpa is a general-purpose tool for the simulation of particle colli- sions at high-energy

### The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations

• Physics
• 2014
A bstractWe discuss the theoretical bases that underpin the automation of the computations of tree-level and next-to-leading order cross sections, of their matching to parton shower simulations, and