30 Aralık 2024 Dergi makalesi Açık Erişim

CERN İşbirliği

Citation Style Language JSON

{
  "DOI": "10.1103/PhysRevLett.133.261803", 
  "abstract": "<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>", 
  "author": [
    {
      "family": "CERN \u0130\u015fbirli\u011fi"
    }
  ], 
  "container_title": "Physical Review Letters", 
  "id": "274471", 
  "issue": "26", 
  "issued": {
    "date-parts": [
      [
        2024, 
        12, 
        30
      ]
    ]
  }, 
  "title": "Simultaneous Unbinned Differential Cross-Section Measurement of Twenty-Four $Z+\\text{jets}$ Kinematic Observables with the ATLAS Detector", 
  "type": "article-journal", 
  "volume": "133"
}