CERN Accelerating science

Article
Report number	arXiv:2108.03926 ; CERN-TH-2022-217
Title	Deep Learning Searches for Vector-Like Leptons at the LHC and Electron/Muon Colliders
Author(s)	Morais, António P. (Aveiro U. ; U. Aveiro (main) ; CERN) ; Onofre, António (LIP, Minho) ; Freitas, Felipe F. (Aveiro U. ; U. Aveiro (main)) ; Gonçalves, João (Aveiro U. ; U. Aveiro (main)) ; Pasechnik, Roman (Lund U.) ; Santos, Rui (Lisbon U., CFTC ; Lisbon, ISEL)
Publication	2023-03-21
Imprint	2021-08-09
Number of pages	26
Note	26 pages, 11 figures, 10 tables, Published version
In:	Eur. Phys. J. C 83 (2023) 232
DOI	10.1140/epjc/s10052-023-11314-3
Subject category	hep-ph ; Particle Physics - Phenomenology
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.
Copyright/License	preprint: (License: CC BY 4.0) publication: © 2023-2025 The Author(s) (License: CC-BY-4.0), sponsored by SCOAP³

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