Jérôme de Favereau, Christophe Delaere, Pavel Demin, Vincent Lemaitre

MoMEMta is a C++ software package to compute Matrix Element weights. Designed in a modular way, it covers the needs of experimental analysis workflows at the LHC. MoMEMta provides working examples for the most common final states (, WW, ...). If you are an expert user, be prepared to feel the freedom of configuring your MEM computation at all levels.
MoMEMta is based on:

- C++, ROOT, Lua scripting language
- Cuba (Monte-Carlo integration library)
- External PDFs (LHAPDF by default)
- External Matrix Elements (currently provided by our MadGraph C++ exporter plugin)

Agni Bethani, Christophe Delaere, Andrea Giammanco, Vincent Lemaitre, Fabio Maltoni

With the 2012 discovery of the Higgs boson at the Large Hadron Collider, LHC, the Standard Model of particle physics has been completed, emerging as a most successful description of matter at the smallest distance scales. But as is always the case, the observation of this particle has also heralded the dawn of a new era in the field: particle physics is now turning to the mysteries posed by the presence of dark matter in the universe, as well as the very existence of the Higgs. The upcoming run of the LHC at 13 TeV will probe possible answers to both issues, providing detailed measurements of the properties of the Higgs and extending significantly the sensitivity to new phenomena.

Since the LHC is the only accelerator currently exploring the energy frontier, it is imperative that the analyses of the collected data use the most powerful possible techniques. In recent years several analyses have utilized multi-variate analysis techniques, obtaining higher sensitivity; yet there is ample room for further improvement. With our program we will import and specialize the most powerful advanced statistical learning techniques to data analyses at the LHC, with the objective of maximizing the chance of new physics discoveries.

We have been part of AMVA4NewPhysics, a network of European institutions whose goal is to foster the development and exploitation of Advanced Multi-Variate Analysis for New Physics searches. The network offered between 2015 and 2019 extensive training in both physics and advanced analysis techniques to graduate students, focusing on providing them with the know-how and the experience to boost their career prospects in and outside academia. The network develops ties with non-academic partners for the creation of interdisciplinary software tools, allowing a successful knowledge transfer in both directions. The network studies innovative techniques and identifies their suitability to problems encountered in searches for new physics at the LHC and detailed studies of the Higgs boson sector.

External collaborators: University of Oxford, INFN, University of Padova, Université Blaise Pascal, LIP, IASA, CERN, UCI, EPFL, B12 Consulting, SDG Consulting, Yandex, MathWorks.

Christophe Delaere

The CMS silicon strip tracker is the largest device of its type ever built. There are 24244 single-sided micro-strip sensors covering an active area of 198m2.
Physics performance of the detector are being constantly assessed and optimized as new data comes.
Members of UCL are playing a major role in the understanding of the silicon strip tracker and in the maintenance and development of the local reconstruction code.

External collaborators: CMS tracker collaboration.