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arXiv · 2004.08262

Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

CMS Collaboration
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Abstract

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 $\sqrt{s} =$ 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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CMS Collaboration. 2020-04-17. Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques. https://doi.org/10.1088/1748-0221%2F15%2F06%2Fp06005

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