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Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
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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.
Forward citations
Cited by 5 Pith papers
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Search for Higgs boson production at high transverse momentum in the WW decay channel in proton-proton collisions at $\sqrt{s}$ = 13 TeV
A first dedicated search for highly Lorentz-boosted H->WW decays at the LHC finds mu = -0.19 +0.48/-0.46, consistent with no signal above background.
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Combination of searches for heavy vector boson resonances in proton-proton collisions at $\sqrt{s}$ = 13 TeV
A CMS combination of searches finds no heavy vector boson resonance and excludes HVT W′/Z′ bosons below 5.5 TeV (weak coupling), 4.8 TeV (strong coupling), and 2.0 TeV for VBF production at 95% CL.
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Enabling stable preservation of ML algorithms in high-energy physics with petrifyML
petrifyML converts lwtnn, TMVA, MVAUtils, and scikit-learn ML models to ONNX or native C++/Python, letting HEP analyses preserve trained algorithms without original frameworks.
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Recent results on searches with boosted Higgs bosons at CMS
A conference proceedings that reviews recent CMS boosted Higgs searches and machine-learning jet taggers without adding a new measurement.
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