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

TL;DR

The paper introduces PaRT, a novel deep learning classifier using self-attention for identifying Lorentz-boosted Higgs bosons decaying to W bosons, improving detection efficiency in CMS LHC data.

Contribution

It presents a new particle transformer model that effectively identifies boosted Higgs to W boson jets, demonstrating strong performance and calibration with real CMS data.

Findings

Achieves over 50% tagging efficiency at 1% background rate.

Maintains decorrelation from jet mass.

Data-to-simulation scale factors range from 0.9 to 1.0 with 7-23% uncertainties.

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.