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        Classifying photons with machine learning in ALICE

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        Bachelorthesis_Rick_Mijsbergh_v4(FINAL).pdf (709.8Kb)
        Publication date
        2019
        Author
        Mijsbergh, R.J.L.
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        Summary
        The ALICE detector at CERN is used to study collisions between heavy ions, which can create a high-energy quark-gluon plasma as they collide inside the detector. In this research the Boosted Decision Tree algorithm is applied to distinguish electron-positron pairs created by the conversion of photons emitted by this plasma, from background consisting of falsely identified ”pairs” of electrons and positrons which do not originate from a photon. The algorithm is trained on over 1.5 million photon candidates generated by a Monte Carlo simulation. Suitable variables for training are determined, data separated into bins to ensure consistency and a K-S test is performed to confirm that the algorithm is not subject to overtraining. Comparison with traditional cuts on the same data show that this BDT method provides a 30% purity increase at maximum significance.
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        https://studenttheses.uu.nl/handle/20.500.12932/32956
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