Deep learning searches for vector-like leptons at the LHC and electron/muon colliders
(2023) In European Physical Journal C 83(3).- 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... (More)
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.
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- author
- Morais, António P. LU ; Onofre, António ; Freitas, Felipe F. ; Gonçalves, João LU ; Pasechnik, Roman LU and Santos, Rui
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- in
- European Physical Journal C
- volume
- 83
- issue
- 3
- article number
- 232
- publisher
- Springer
- external identifiers
-
- scopus:85150903684
- ISSN
- 1434-6044
- DOI
- 10.1140/epjc/s10052-023-11314-3
- language
- English
- LU publication?
- yes
- id
- c1229d1e-bdd3-4757-a658-277a2cfd0a14
- date added to LUP
- 2023-05-23 12:47:03
- date last changed
- 2023-05-23 12:47:03
@article{c1229d1e-bdd3-4757-a658-277a2cfd0a14, abstract = {{<p>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.</p>}}, author = {{Morais, António P. and Onofre, António and Freitas, Felipe F. and Gonçalves, João and Pasechnik, Roman and Santos, Rui}}, issn = {{1434-6044}}, language = {{eng}}, number = {{3}}, publisher = {{Springer}}, series = {{European Physical Journal C}}, title = {{Deep learning searches for vector-like leptons at the LHC and electron/muon colliders}}, url = {{http://dx.doi.org/10.1140/epjc/s10052-023-11314-3}}, doi = {{10.1140/epjc/s10052-023-11314-3}}, volume = {{83}}, year = {{2023}}, }