Computational Particle Physics for Event Generators and Data Analysis
Denis Perret-Gallix

TL;DR
This paper reviews computational techniques and tools for particle physics event generation and data analysis, emphasizing the importance of automatic matrix element calculations at various orders for LHC experiments.
Contribution
It provides a comprehensive overview of the development and application of automatic matrix element computation methods in high-energy physics.
Findings
Advances in NLO calculation techniques enable more complex process simulations.
Automatic tools are crucial for accurate event generation at LHC.
Higher-order calculations remain computationally challenging but essential for precision.
Abstract
High-energy physics data analysis relies heavily on the comparison between experimental and simulated data as stressed lately by the Higgs search at LHC and the recent identification of a Higgs-like new boson. The first link in the full simulation chain is the event generation both for background and for expected signals. Nowadays event generators are based on the automatic computation of matrix element or amplitude for each process of interest. Moreover, recent analysis techniques based on the matrix element likelihood method assign probabilities for every event to belong to any of a given set of possible processes. This method originally used for the top mass measurement, although computing intensive, has shown its power at LHC to extract the new boson signal from the background. Serving both needs, the automatic calculation of matrix element is therefore more than ever of prime…
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