Deep learning searches for vector-like leptons at the LHC and electron/muon colliders
Abstract 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 signature...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-03-01
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| Series: | European Physical Journal C: Particles and Fields |
| Online Access: | https://doi.org/10.1140/epjc/s10052-023-11314-3 |
| _version_ | 1797836290147745792 |
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| author | António P. Morais António Onofre Felipe F. Freitas João Gonçalves Roman Pasechnik Rui Santos |
| author_facet | António P. Morais António Onofre Felipe F. Freitas João Gonçalves Roman Pasechnik Rui Santos |
| author_sort | António P. Morais |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-09T15:07:32Z |
| format | Article |
| id | doaj.art-7f194ca4a3204f629ecd7159f480b89e |
| institution | Directory Open Access Journal |
| issn | 1434-6052 |
| language | English |
| last_indexed | 2024-04-09T15:07:32Z |
| publishDate | 2023-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Physical Journal C: Particles and Fields |
| spelling | doaj.art-7f194ca4a3204f629ecd7159f480b89e2023-04-30T11:25:26ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60522023-03-0183312410.1140/epjc/s10052-023-11314-3Deep learning searches for vector-like leptons at the LHC and electron/muon collidersAntónio P. Morais0António Onofre1Felipe F. Freitas2João Gonçalves3Roman Pasechnik4Rui Santos5Departamento de Física da Universidade de Aveiro and Centre for Research and Development in Mathematics and Applications (CIDMA)Departamento de Física da Universidade do MinhoDepartamento de Física da Universidade de Aveiro and Centre for Research and Development in Mathematics and Applications (CIDMA)Departamento de Física da Universidade de Aveiro and Centre for Research and Development in Mathematics and Applications (CIDMA)Department of Physics, Lund UniversityCentro de Física Teórica e Computacional, Faculdade de Ciências, Universidade de LisboaAbstract 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.https://doi.org/10.1140/epjc/s10052-023-11314-3 |
| spellingShingle | António P. Morais António Onofre Felipe F. Freitas João Gonçalves Roman Pasechnik Rui Santos Deep learning searches for vector-like leptons at the LHC and electron/muon colliders European Physical Journal C: Particles and Fields |
| title | Deep learning searches for vector-like leptons at the LHC and electron/muon colliders |
| title_full | Deep learning searches for vector-like leptons at the LHC and electron/muon colliders |
| title_fullStr | Deep learning searches for vector-like leptons at the LHC and electron/muon colliders |
| title_full_unstemmed | Deep learning searches for vector-like leptons at the LHC and electron/muon colliders |
| title_short | Deep learning searches for vector-like leptons at the LHC and electron/muon colliders |
| title_sort | deep learning searches for vector like leptons at the lhc and electron muon colliders |
| url | https://doi.org/10.1140/epjc/s10052-023-11314-3 |
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