Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
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Iop Publishing Ltd
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Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at root S = 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1). Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency.
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Large detector-systems performance, Pattern recognition, cluster finding, calibration and fitting methods
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Inglês
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Journal Of Instrumentation. Bristol: Iop Publishing Ltd, v. 15, n. 6, 87 p., 2020.
