Progress in ${\cal CP}$ violating top-Higgs coupling at the LHC with Machine Learning

Fuente: arXiv
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Auteurs principaux: Hammad, A., Jueid, Adil
Format: Preprint
Publié: 2025
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author Hammad, A.
Jueid, Adil
author_facet Hammad, A.
Jueid, Adil
contents A precise measurement of the top-Higgs coupling is essential in particle physics, as it offers a powerful probe of potential new physics beyond the Standard Model (BSM), particularly scenarios involving ${\cal CP}$ violation, which is a key condition in addressing the problem of baryon asymmetry of the universe. In this article, we review the recent progress in the studies of the the top-Higgs coupling at the Large Hadron Collider (LHC). We briefly highlight the recent Machine Learning (ML) algorithms being used and their role in constraining the ${\cal CP}$ phase of the top-Higgs coupling with an emphasis on the future potential of beyond-the-traditional methods such as transformers and heterogeneous graphs in these studies.
format Preprint
id arxiv_https___arxiv_org_abs_2504_11791
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Progress in ${\cal CP}$ violating top-Higgs coupling at the LHC with Machine Learning
Hammad, A.
Jueid, Adil
High Energy Physics - Phenomenology
A precise measurement of the top-Higgs coupling is essential in particle physics, as it offers a powerful probe of potential new physics beyond the Standard Model (BSM), particularly scenarios involving ${\cal CP}$ violation, which is a key condition in addressing the problem of baryon asymmetry of the universe. In this article, we review the recent progress in the studies of the the top-Higgs coupling at the Large Hadron Collider (LHC). We briefly highlight the recent Machine Learning (ML) algorithms being used and their role in constraining the ${\cal CP}$ phase of the top-Higgs coupling with an emphasis on the future potential of beyond-the-traditional methods such as transformers and heterogeneous graphs in these studies.
title Progress in ${\cal CP}$ violating top-Higgs coupling at the LHC with Machine Learning
topic High Energy Physics - Phenomenology
url https://arxiv.org/abs/2504.11791