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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 9 Pith papers
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Measurement of the jet mass in hadronic decays of boosted W bosons at 13 TeV and extraction of the W boson mass
Unfolded double-differential W+jets cross section versus jet p_T and soft-drop mass yields m_W = 80.83 ± 0.55 GeV, the most precise all-jets extraction at a hadron collider.
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Search for a resonance in events with four top quarks decaying into two leptons and jets in proton-proton collisions
No significant excess is observed in a search for top-philic resonances in four-top-quark events with two leptons, and 95% confidence-level upper limits are set for mediator masses from 500 GeV to 4 TeV.
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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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Graph theory inspired anomaly detection at the LHC
Sparse globally rigid graph representations of jets, combined with roughly 30 reclustered subjets, improve graph autoencoder anomaly detection on the LHC Olympics benchmark.
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Jet Substructure Probe on Scalar Leptoquark Models via Top Polarization
A simulation study projects up to 5.4 sigma discovery significance for scalar leptoquarks at the HL-LHC and up to 3.2 sigma separation between the S3 and R2 models using a BDT score.
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Transformer networks for Heavy flavor jet tagging
A review of transformer-based jet tagging that highlights the authors' CA-Mixer network as a state-of-the-art, faster alternative to Particle Transformer.
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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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