Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
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Type :
Article
Publication Status :
Published
Access :
openAccess
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 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.
Source :
Journal of Instrumentation
Date :
2020-06
Volume :
15
Issue :
6
Publisher :
IOP Publishing
URI
http://hdl.handle.net/10679/7149https://iopscience.iop.org/article/10.1088/1748-0221/15/06/P06005
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