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Inclusive Jet Measurements in Pb-Pb Collisions at 5.02 TeV with ALICE using Machine Learning Techniques

These proceedings report on measurements of the jet spectrum and nuclear modification factor for inclusive full jets (containing both charged and neutral constituents) in Pb--Pb and pp collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV recorded with the ALICE detector. These measurements use a machine lea...

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Autor principal: Bossi, Hannah
Lenguaje:eng
Publicado: 2020
Materias:
Acceso en línea:https://dx.doi.org/10.22323/1.387.0135
http://cds.cern.ch/record/2729915
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author Bossi, Hannah
author_facet Bossi, Hannah
author_sort Bossi, Hannah
collection CERN
description These proceedings report on measurements of the jet spectrum and nuclear modification factor for inclusive full jets (containing both charged and neutral constituents) in Pb--Pb and pp collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV recorded with the ALICE detector. These measurements use a machine learning based background correction, which reduces residual fluctuations. This method allows for measurements to lower transverse momenta and larger jet resolution parameter (R) than previously possible in ALICE. In this method, machine learning techniques are used to correct the jet transverse momentum on a jet-by-jet basis using jet parameters such as information about the constituents of the jet. Studies that investigate the effect of the potential fragmentation bias introduced by learning from constituents will also be discussed. With these studies in mind, the results are compared to theoretical predictions.
id cern-2729915
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2020
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spelling cern-27299152022-09-08T02:32:40Zdoi:10.22323/1.387.0135http://cds.cern.ch/record/2729915engBossi, HannahInclusive Jet Measurements in Pb-Pb Collisions at 5.02 TeV with ALICE using Machine Learning Techniquesnucl-exNuclear Physics - ExperimentThese proceedings report on measurements of the jet spectrum and nuclear modification factor for inclusive full jets (containing both charged and neutral constituents) in Pb--Pb and pp collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV recorded with the ALICE detector. These measurements use a machine learning based background correction, which reduces residual fluctuations. This method allows for measurements to lower transverse momenta and larger jet resolution parameter (R) than previously possible in ALICE. In this method, machine learning techniques are used to correct the jet transverse momentum on a jet-by-jet basis using jet parameters such as information about the constituents of the jet. Studies that investigate the effect of the potential fragmentation bias introduced by learning from constituents will also be discussed. With these studies in mind, the results are compared to theoretical predictions.These proceedings report on measurements of the jet spectrum and nuclear modification factor for inclusive full jets (containing both charged and neutral constituents) in Pb--Pb and pp collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV recorded with the ALICE detector. These measurements use a machine learning based background correction, which reduces residual fluctuations. This method allows for measurements to lower transverse momenta and larger jet resolution parameter (R) than previously possible in ALICE. In this method, machine learning techniques are used to correct the jet transverse momentum on a jet-by-jet basis using jet parameters such as information about the constituents of the jet. Studies that investigate the effect of the potential fragmentation bias introduced by learning from constituents will also be discussed. With these studies in mind, the results are compared to theoretical predictions. These proceedings report on measurements of the jet spectrum and nuclear modification factor for inclusive full jets (containing both charged and neutral constituents) in Pb-Pb and pp collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV recorded with the ALICE detector. These measurements use a machine learning based background correction, which reduces residual fluctuations. This method allows for measurements to lower transverse momenta and larger jet resolution parameter (R) than previously possible in ALICE. In this method, machine learning techniques are used to correct the jet transverse momentum on a jet-by-jet basis using jet parameters such as information about the constituents of the jet. Studies that investigate the effect of the potential fragmentation bias introduced by learning from constituents will also be discussed. With these studies in mind, the results are compared to theoretical predictions.arXiv:2009.02269oai:cds.cern.ch:27299152020-09-04
spellingShingle nucl-ex
Nuclear Physics - Experiment
Bossi, Hannah
Inclusive Jet Measurements in Pb-Pb Collisions at 5.02 TeV with ALICE using Machine Learning Techniques
title Inclusive Jet Measurements in Pb-Pb Collisions at 5.02 TeV with ALICE using Machine Learning Techniques
title_full Inclusive Jet Measurements in Pb-Pb Collisions at 5.02 TeV with ALICE using Machine Learning Techniques
title_fullStr Inclusive Jet Measurements in Pb-Pb Collisions at 5.02 TeV with ALICE using Machine Learning Techniques
title_full_unstemmed Inclusive Jet Measurements in Pb-Pb Collisions at 5.02 TeV with ALICE using Machine Learning Techniques
title_short Inclusive Jet Measurements in Pb-Pb Collisions at 5.02 TeV with ALICE using Machine Learning Techniques
title_sort inclusive jet measurements in pb-pb collisions at 5.02 tev with alice using machine learning techniques
topic nucl-ex
Nuclear Physics - Experiment
url https://dx.doi.org/10.22323/1.387.0135
http://cds.cern.ch/record/2729915
work_keys_str_mv AT bossihannah inclusivejetmeasurementsinpbpbcollisionsat502tevwithaliceusingmachinelearningtechniques