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.

Simultaneous Unbinned Differential Cross-Section Measurement of Twenty-Four Z+jets Kinematic Observables with the ATLAS Detector

M Centonze;G Chiodini;Francesco De Santis;E Gorini;S Grancagnolo;FG Gravili;M Greco;A Palazzo;M Primavera;S Spagnolo;A Ventura;
2024-01-01

Abstract

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.
File in questo prodotto:
File Dimensione Formato  
prl133h.pdf

accesso aperto

Descrizione: Articolo
Tipologia: Versione editoriale
Licenza: Creative commons
Dimensione 1.47 MB
Formato Adobe PDF
1.47 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11587/542046
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact