Dergi makalesi Açık Erişim
CERN İşbirliği
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<identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/274471</identifier>
<creators>
<creator>
<creatorName>CERN İşbirliği</creatorName>
<affiliation>CERN</affiliation>
</creator>
</creators>
<titles>
<title>Simultaneous Unbinned Differential Cross-Section Measurement Of Twenty-Four $Z+\Text{Jets}$ Kinematic Observables With The Atlas Detector</title>
</titles>
<publisher>Aperta</publisher>
<publicationYear>2024</publicationYear>
<dates>
<date dateType="Issued">2024-12-30</date>
</dates>
<resourceType resourceTypeGeneral="Text">Journal article</resourceType>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/274471</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1103/PhysRevLett.133.261803</relatedIdentifier>
</relatedIdentifiers>
<rightsList>
<rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="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></description>
</descriptions>
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