dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Sas, M. | |
dc.contributor.advisor | Peitzmann, T. | |
dc.contributor.author | Schaapherder, T. | |
dc.date.accessioned | 2018-10-07T17:01:12Z | |
dc.date.available | 2018-10-07T17:01:12Z | |
dc.date.issued | 2018 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/37588 | |
dc.description.abstract | The ALICE detector located at CERN studies subatomic particles produced in heavy-ion collisions. These collisions generate enormous amounts of particles including photons. The data gathered from these collisions is contaminated with background. This research focuses on generating a viable and efficient machine-learning algorithm for selecting photon conversions and discriminating them from background. The method used is that of the boosted decision tree (BDT). A Monte Carlo simulation is used to train and test the BDT and afterwards to test the performance of the BDT. The Monte Carlo simulation consists of data taken from a simulated collision of 40%-60% centrality and a center of mass energy of 2.76$ TeV (Tera electron Volts). | |
dc.description.sponsorship | Utrecht University | |
dc.format.extent | 2668836 | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.title | Photon Conversion Classi?cation by Boosting Decision Trees | |
dc.type.content | Bachelor Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | ALICE, Boosted decision tree, conversion photon classification, photons, LHC, TMVA, machine learning | |
dc.subject.courseuu | Natuur- en Sterrenkunde | |