Using Machine Learning to Improve PDF Uncertainties
High Energy Physics - Phenomenology
2024-01-25 v1
Abstract
Parton Distribution Functions (PDFs) contribute significantly to the uncertainty on the determination of the top-quark pole mass and other precision measurements at the Large Hadron Collider (LHC). It is crucial to understand these uncertainties and reduce them to obtain the next generation of precision measurements at the LHC. The region of high momentum fraction offers an opportunity to make improvements to the PDFs. This study uses machine learning techniques in production to target this region of the PDF set and has potential to significantly reduce its uncertainty.
Keywords
Cite
@article{arxiv.2401.13050,
title = {Using Machine Learning to Improve PDF Uncertainties},
author = {Jason P. Gombas and Reinhard Schwienhorst and Binbin Dong and Jarrett Fein},
journal= {arXiv preprint arXiv:2401.13050},
year = {2024}
}
Comments
Poster presentation at the 16th International Workshop on Top Quark Physics (Top2023), 24-29 September 2023