Particle transformers for identifying Lorentz-boosted Higgs bosons decaying to a pair of W bosons
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914464699252736 |
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| author | CMS Collaboration |
| author_facet | CMS Collaboration |
| contents | 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$-$1.0 range, with relative uncertainties between 7 and 23%. The tagging capability of PaRT enhances the sensitivity of standard model measurements and searches for beyond-the-standard-model resonances decaying to hadronic diboson systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09809 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Particle transformers for identifying Lorentz-boosted Higgs bosons decaying to a pair of W bosons CMS Collaboration High Energy Physics - Experiment Instrumentation and Detectors A novel deep neural network classifier, a ``Particle transformer'' (PaRT), is introduced for the identification of highly Lorentz-boosted resonances reconstructed as single, multipronged jets in measurements and searches performed by the CMS Collaboration at the CERN LHC. Based on a self-attention mechanism that allows the model to weigh the importance of different particles, PaRT is trained on a wide variety of topologies, notably demonstrating strong performance for the first time on jets originating from boosted Higgs boson decays to W bosons. The PaRT algorithm achieves a tagging efficiency of more than 50\% for such jets at a background efficiency of 1%, while maintaining decorrelation from the jet mass. A calibration is performed in proton-proton collision data collected by CMS at a center-of-mass energy of 13 TeV, with a data set corresponding to a total luminosity of 138 fb$^{-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. |
| title | Particle transformers for identifying Lorentz-boosted Higgs bosons decaying to a pair of W bosons |
| topic | High Energy Physics - Experiment Instrumentation and Detectors |
| url | https://arxiv.org/abs/2604.09809 |