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A machine learning driven approach to the MLM merging algorithm

Puget, William
(2024)

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Puget_07081700_2024.pdf
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Details

Supervisors
Mattelaer, Olivier ; Maltoni, Fabio
Faculty
Faculté des sciences
Degree label
Master [120] en sciences physiques, à finalité approfondie
Abstract
In simulation of high energy particle collisions, one of the most used tools to perform jet merging at leading order is the MLM algorithm. Despite its strengths, the veto it imposes on a significant part of the fully generated events implies that we waste a non-negligible amount of computation. The purpose of this work is to explore, using machine learning, ways in which we could reduce this drawback of the MLM algorithm. We ultimately show that for proton-proton collisions that create quark-antiquark pairs, we can create MLM-like merging results without actually applying the veto.

Citations

  • APA

Puget, W. (2024). A machine learning driven approach to the MLM merging algorithm. https://dial-mem.test.bib.ucl.ac.be/handle/2078.2/41805