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Deep learning searches for vector-like leptons at the LHC and electron/muon colliders

Morais, António P. LU ; Onofre, António ; Freitas, Felipe F. ; Gonçalves, João LU ; Pasechnik, Roman LU and Santos, Rui (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
; ; ; ; and
organization
publishing date
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}},
}