Resolved photoproduction in MadGraph5_aMC@NLO

The upcoming Electron-Ion Collider (EIC), with its high luminosity, will offer an unprecedented opportunity to explore the internal structure of atomic nucleus over an extended energy range from $\sqrt{s_{ep}} =$ 45 GeV to $\sqrt{s_{ep}} =$ 140 GeV. A particularly promising aspect of this collider is the study of the partonic structure with quasi-real photons which can also be studied in inclusive ultra-peripheral collisions at the Large Hadron Collider (LHC). In this work, we present our validation of resolved photoproduction at fixed order for (next-to-) leading order using MadGraph5_aMC@NLO, a widely adopted framework at the LHC.

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Publication Details

Published
2024-12-27
DOI
https://doi.org/10.22323/1.469.0185
Primary Topic
Advanced Graph Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

Resolved photoproduction in MadGraph5_aMC@NLO

A. N. Safronov, Carlo Flore, D. P. Kikoła, Jean-Philippe Lansberg et al.
Advanced Graph Neural Networks
article

Resolved photoproduction in MadGraph5_aMC@NLO

A. N. Safronov, Carlo Flore, D. P. Kikoła, Jean-Philippe Lansberg, Laboni Manna, Olivier Mattelaer
article en

Abstract

The upcoming Electron-Ion Collider (EIC), with its high luminosity, will offer an unprecedented opportunity to explore the internal structure of atomic nucleus over an extended energy range from $\sqrt{s_{ep}} =$ 45 GeV to $\sqrt{s_{ep}} =$ 140 GeV. A particularly promising aspect of this collider is the study of the partonic structure with quasi-real photons which can also be studied in inclusive ultra-peripheral collisions at the Large Hadron Collider (LHC). In this work, we present our validation of resolved photoproduction at fixed order for (next-to-) leading order using MadGraph5_aMC@NLO, a widely adopted framework at the LHC.

Strong, European Commission, Agence Nationale de la Recherche, Fonds De La Recherche Scientifique - FNRS, Politechnika Warszawska, Centre National de la Recherche Scientifique, Institut National de Physique Nucléaire et de Physique des Particules
Openalex Percentile: Top 78%
Advanced Graph Neural Networks
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