30 Aralık 2024 Dergi makalesi Açık Erişim

CERN İşbirliği

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    "description": "<p>$Z$ 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 $Z+\\text{jets}$ observables using $139\\text{}\\text{}{\\mathrm{fb}}^{-1}$ of proton-proton collisions at $\\sqrt{s}=13\\text{}\\text{}\\mathrm{TeV}$ 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 reuse in a variety of contexts and for new observables to be constructed from the twenty-four measured observables.</p>", 
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      "title": "Physical Review Letters", 
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    "title": "Simultaneous Unbinned Differential Cross-Section Measurement of Twenty-Four $Z+\\text{jets}$ Kinematic Observables with the ATLAS Detector"
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