Jet substructure probe on scalar leptoquark models via top polarization
Abstract The study of leptoquarks and their couplings to fermions with different chiralities provides a powerful tool for distinguishing among different leptoquark models. As a case study, we focus on two specific third-generation scalar leptoquark models, S 3 and R 2, which differ in their electrow...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-07-01
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| Series: | Journal of High Energy Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/JHEP07(2025)145 |
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| Summary: | Abstract The study of leptoquarks and their couplings to fermions with different chiralities provides a powerful tool for distinguishing among different leptoquark models. As a case study, we focus on two specific third-generation scalar leptoquark models, S 3 and R 2, which differ in their electroweak quantum numbers and chiral structures of couplings to the top quark, leading to distinct top-quark polarization states. To enhance the efficacy of the analysis, we employ jet substructure techniques like Soft Drop, N-subjettiness, and our custom b-tagging method, along with other event variables. The analysis has been performed using both fixed radius and dynamic radius jet clustering algorithms. A multivariate analysis using a boosted decision tree (BDT) is performed to isolate signal from the Standard Model background. For a leptoquark mass of 1250 GeV, the analysis achieves a signal significance of up to 5.3 σ at the 14 TeV HL-LHC. Furthermore, a CL s -based profile likelihood estimator is applied to polarization-sensitive variables to discriminate between the two models. To enhance separation between the two models, an additional BDT classifier score is obtained by training a BDT network to distinguish between the S 3 and R 2 models. In the chosen signal region, the BDT classifier score provides a separation score of up to 3.2 σ, outperforming traditional variables such as E b /E t and cos θ b . |
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| ISSN: | 1029-8479 |