Particle transformers for identifying Lorentz-boosted Higgs bosons decaying to a pair of W bosons | CU Experts

; A; bstract; ; ; A novel deep neural network classifier, a “particle transformer” (P; ar; T), is introduced for the identification of highly Lorentz-boosted resonances reconstructed as single, multipronged jets in measurements and searches performed by the CMS Collaboration at the CERN LHC. Based on a self-attention mechanism that allows the model to weigh the importance of different particles, P; ar; T is trained on a wide variety of topologies, notably demonstrating strong performance for the first time on jets originating from boosted Higgs boson decays to W bosons. The P; ar; T algorithm achieves a tagging efficiency of more than 50% for such jets at a background efficiency of 1%, while maintaining decorrelation from the jet mass. A calibration is performed in proton-proton collision data collected by CMS at a center-of-mass energy of 13 TeV, with a data set corresponding to a total luminosity of 138 fb; ; −; 1; ; . Data-to-simulation selection efficiency scale factors are measured to be in the 0.9–1.0 range, with relative uncertainties between 7 and 23%. The tagging capability of P; ar; T enhances the sensitivity of standard model measurements and searches for beyond-the-standard-model resonances decaying to hadronic diboson systems.;

Hayrapetyan A; Makarenko V; Tumasyan A; Adam W; Benato L; Bergauer T; Dragicevic M; Giordano C; Hussain PS; Jeitler M

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