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

Abstract

A novel deep neural network classifier, a “particle transformer” (ParT), 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, ParT 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 ParT 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 ParT enhances the sensitivity of standard model measurements and searches for beyond-the-standard-model resonances decaying to hadronic diboson systems.