CBM Performance for Λ0 Hyperon Yield Measurements Using Machine Learning Techniques

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dc.contributor.advisor Schmidt, Hans Rudolf (Prof. Dr.)
dc.contributor.author Khan, Shahid
dc.date.accessioned 2023-05-31T13:23:33Z
dc.date.available 2023-05-31T13:23:33Z
dc.date.issued 2023-05-31
dc.identifier.uri http://hdl.handle.net/10900/141561
dc.identifier.uri http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1415612 de_DE
dc.identifier.uri http://dx.doi.org/10.15496/publikation-82908
dc.description.abstract The phase diagram of the QCD matter in the baryon chemical potential region 500 MeV ≤ μB ≤ 800 MeV will be studied by the future Compressed Baryonic Matter (CBM) experiment in the beam energy range corresponding to √sNN = 2.9 - 4.9 GeV. The experiment will be carried out at the Facility for Anti-Proton and Ion Research. A prerequisite for determining the properties of dense baryonic matter is the multi-differential measurement of the yield of (multi-) strange hadrons. This work evaluates the performance of CBM in measuring the multi-differential (charged tracks multiplicity, transverse momentum, and rapidity) yield of the most abundantly produced Λ0 baryon for Au-Au collisions at a beam momentum of 12 AGeV/c. The Kalman Filter algorithm is employed to reconstruct the Λ0 baryon through its weak decay to a proton and π- topology, which is selected non-linearly using the machine learning algorithm eXtreme Gradient Boosting (XGBoost). The selection is performed multi-differentially in transverse momentum and rapidity for the multiplicity interval [200,400] of charged tracks to achieve a high signal-to-background ratio. After the selection, raw-yield extraction is performed multi-differentially through a multi-step fitting routine. The extracted raw-yield is corrected for the efficiency of the reconstruction and selection procedure and the geometrical acceptance of the experiment. The corrected yield is compared to the true yield to validate the reconstruction, selection, yield extraction, and correction procedure. The systematic uncertainties are evaluated by varying the selection parameters and they are typically below 3% but can go up to 6% for high transverse momentum intervals. en
dc.language.iso en de_DE
dc.publisher Universität Tübingen de_DE
dc.rights ubt-podok de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_mit_pod.php?la=de de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_mit_pod.php?la=en en
dc.subject.ddc 530 de_DE
dc.subject.other heavy-ions collision en
dc.subject.other machine learning en
dc.subject.other CBM en
dc.title CBM Performance for Λ0 Hyperon Yield Measurements Using Machine Learning Techniques en
dc.type Dissertation de_DE
dcterms.dateAccepted 2023-05-23
utue.publikation.fachbereich Physik de_DE
utue.publikation.fakultaet 7 Mathematisch-Naturwissenschaftliche Fakultät de_DE
utue.publikation.noppn yes de_DE


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