https://doi.org/10.1140/epjc/s10052-023-11314-3
Regular Article - Theoretical Physics
Deep learning searches for vector-like leptons at the LHC and electron/muon colliders
1
Departamento de Física da Universidade de Aveiro and Centre for Research and Development in Mathematics and Applications (CIDMA), 3810-183, Aveiro, Portugal
2
Theoretical Physics Department, CERN, 1211, Geneva 23, Switzerland
3
Departamento de Física da Universidade do Minho, 4710-057, Braga, Portugal
4
Department of Physics, Lund University, Sölvegatan 14A, 223-62, Lund, Sweden
5
Centro de Física Teórica e Computacional, Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisbon, Portugal
6
ISEL-Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, 1959-007, Lisbon, Portugal
Received:
5
August
2022
Accepted:
12
February
2023
Published online:
21
March
2023
The discovery potential of both singlet and doublet vector-like leptons (VLLs) at the Large Hadron Collider (LHC) as well as at the not-so-far future muon and electron machines is explored. The focus is on a single production channel for LHC direct searches while double production signatures are proposed for the leptonic colliders. A Deep Learning algorithm to determine the discovery (or exclusion) statistical significance at the LHC is employed. While doublet VLLs can be probed up to masses of 1 TeV, their singlet counterparts have very low cross sections and can hardly be tested beyond a few hundreds of GeV at the LHC. This motivates a physics-case analysis in the context of leptonic colliders where one obtains larger cross sections in VLL double production channels, allowing to probe higher mass regimes otherwise inaccessible even to the LHC high-luminosity upgrade.
© The Author(s) 2023
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