Abstract A model based on a$$U(1)_{T^3_R}$$ extension of the Standard Model can address the mass hierarchy between generations of fermions, explain thermal dark matter abundance, and the muon$$g - 2$$ ,$$R_{(D)}$$ , and$$R_{(D^*)}$$ anomalies. The model contains a light scalar boson$$\phi '$$ and a heavy vector-like quark$$\chi _\textrm{u}$$ that can be probed at CERN’s large hadron collider (LHC). We perform a phenomenology study on the production of$$\phi '$$ and$${\chi }_u$$ particles from proton–proton$$(\textrm{pp})$$ collisions at the LHC at$$\sqrt{s}=13.6$$ TeV, primarily through$$g{-g}$$ and$$t{-\chi _\textrm{u}}$$ fusion. We work under a simplified model approach and directly take the$$\chi _\textrm{u}$$ and$$\phi '$$ masses as free parameters. We perform a phenomenological analysis considering$$\chi _\textrm{u}$$ final states to b-quarks, muons, and neutrinos, and$$\phi '$$ decays to$$\mu ^+\mu ^-$$ . A machine learning algorithm is used to maximize the signal sensitivity, considering an integrated luminosity of 3000$$\text {fb}^{-1}$$ . The proposed methodology can be a key mode for discovery over a large mass range, including low masses, traditionally considered difficult due to experimental constraints.
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Regularity Criteria for the Kuramoto–Sivashinsky Equation in Dimensions Two and Three
Abstract We propose and prove several regularity criteria for the 2D and 3D Kuramoto–Sivashinsky equation, in both its scalar and vector forms. In particular, we examine integrability criteria for the regularity of solutions in terms of the scalar solution$$\phi $$ , the vector solution$$u\triangleq \nabla \phi $$ , as well as the divergence$$\text {div}(u)=\Delta \phi $$ , and each component ofuand$$\nabla u$$ . We also investigate these criteria computationally in the 2D case, and we include snapshots of solutions for several quantities of interest that arise in energy estimates.
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- Award ID(s):
- 1953346
- PAR ID:
- 10371259
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Journal of Nonlinear Science
- Volume:
- 32
- Issue:
- 6
- ISSN:
- 0938-8974
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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