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Bibliographic Details
Main Author: ATLAS Collaboration
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2405.20041
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author ATLAS Collaboration
author_facet ATLAS Collaboration
contents $Z$ boson events at the Large Hadron Collider can be selected with high purity and are sensitive to a diverse range of QCD phenomena. As a result, these events are often used to probe the nature of the strong force, improve Monte Carlo event generators, and search for deviations from Standard Model predictions. All previous measurements of $Z$ boson production characterize the event properties using a small number of observables and present the results as differential cross sections in predetermined bins. In this analysis, a machine learning method called OmniFold is used to produce a simultaneous measurement of twenty-four $Z$+jets observables using $139$ fb$^{-1}$ of proton-proton collisions at $\sqrt{s}=13$ TeV collected with the ATLAS detector. Unlike any previous fiducial differential cross-section measurement, this result is presented unbinned as a dataset of particle-level events, allowing for flexible re-use in a variety of contexts and for new observables to be constructed from the twenty-four measured observables.
format Preprint
id arxiv_https___arxiv_org_abs_2405_20041
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A simultaneous unbinned differential cross section measurement of twenty-four $Z$+jets kinematic observables with the ATLAS detector
ATLAS Collaboration
High Energy Physics - Experiment
$Z$ boson events at the Large Hadron Collider can be selected with high purity and are sensitive to a diverse range of QCD phenomena. As a result, these events are often used to probe the nature of the strong force, improve Monte Carlo event generators, and search for deviations from Standard Model predictions. All previous measurements of $Z$ boson production characterize the event properties using a small number of observables and present the results as differential cross sections in predetermined bins. In this analysis, a machine learning method called OmniFold is used to produce a simultaneous measurement of twenty-four $Z$+jets observables using $139$ fb$^{-1}$ of proton-proton collisions at $\sqrt{s}=13$ TeV collected with the ATLAS detector. Unlike any previous fiducial differential cross-section measurement, this result is presented unbinned as a dataset of particle-level events, allowing for flexible re-use in a variety of contexts and for new observables to be constructed from the twenty-four measured observables.
title A simultaneous unbinned differential cross section measurement of twenty-four $Z$+jets kinematic observables with the ATLAS detector
topic High Energy Physics - Experiment
url https://arxiv.org/abs/2405.20041