Analysis of the q\bar q\to Z^* \to hA \to4τ process within the lepton-specific 2HDM at the LHC
Analysis of the q\bar q\to Z^* \to hA \to4τ process within the lepton-specific 2HDM at the LHC
We analyse light Higgs scalar and pseudoscalar associated hadro-production in the 2-Higgs Doublet Model (2HDM) Type-X (or lepton-specific) within the parameter space allowed by theoretical self-consistency requirements as well as the latest experimental constraints from the Large Hadron Collider (LHC), precision data and B physics. Over the viable regions of such a scenario, the Standard Model-like Higgs boson discovered at the LHC in 2012 is the heavier CP-even state H. Furthermore, in the Type-X scenario, due to large \tan\beta, the lighter Higgs scalar h and the pseudoscalar A mainly decay into two \tau leptons. Therefore, we concentrate on analysing the signal process pp\to Z^{*} \to hA\to \tau^{+}\tau^{-}\tau^{+}\tau^{-}\to \ell \nu_\ell \ell \nu_\ell \tau_h \tau_h (where \ell= e, \mu whereas \tau_h represents the hadronic decay of the \tau) and explore the feasibility of conducting such a search at the LHC with a centre-of-mass energy of \sqrt{s}~= 14 TeV and a luminosity of L~=~300~fb^{-1}. To suppress the huge SM background, we confine ourselves to consider the fraction of signal events with two same-sign \tau leptons further decaying into same-sign leptons while the other two \tau leptons decay hadronically. We find that a combination of kinematical selection and machine learning (ML) analysis will yields significant sensitivity to this process at the end of the LHC Run 3.
hep-ph
Ma, Yan
e0ce37a0-0ba3-47f6-b4ed-1d86e2c0ba10
Arhrib, A.
a5a1d42e-13fd-40f5-9401-496a46eb671f
Moretti, S.
b57cf0f0-4bc3-4e02-96e3-071255366614
Semlali, S.
cf9bc9ea-2f58-49dc-9645-5b158be9ffe6
Wang, Y.
ee7c795e-9a1e-438d-8c72-5d1c75986814
Yan, Q.S.
21a83587-e369-4981-b8ed-8f752573c7f6
Ma, Yan
e0ce37a0-0ba3-47f6-b4ed-1d86e2c0ba10
Arhrib, A.
a5a1d42e-13fd-40f5-9401-496a46eb671f
Moretti, S.
b57cf0f0-4bc3-4e02-96e3-071255366614
Semlali, S.
cf9bc9ea-2f58-49dc-9645-5b158be9ffe6
Wang, Y.
ee7c795e-9a1e-438d-8c72-5d1c75986814
Yan, Q.S.
21a83587-e369-4981-b8ed-8f752573c7f6
[Unknown type: UNSPECIFIED]
Abstract
We analyse light Higgs scalar and pseudoscalar associated hadro-production in the 2-Higgs Doublet Model (2HDM) Type-X (or lepton-specific) within the parameter space allowed by theoretical self-consistency requirements as well as the latest experimental constraints from the Large Hadron Collider (LHC), precision data and B physics. Over the viable regions of such a scenario, the Standard Model-like Higgs boson discovered at the LHC in 2012 is the heavier CP-even state H. Furthermore, in the Type-X scenario, due to large \tan\beta, the lighter Higgs scalar h and the pseudoscalar A mainly decay into two \tau leptons. Therefore, we concentrate on analysing the signal process pp\to Z^{*} \to hA\to \tau^{+}\tau^{-}\tau^{+}\tau^{-}\to \ell \nu_\ell \ell \nu_\ell \tau_h \tau_h (where \ell= e, \mu whereas \tau_h represents the hadronic decay of the \tau) and explore the feasibility of conducting such a search at the LHC with a centre-of-mass energy of \sqrt{s}~= 14 TeV and a luminosity of L~=~300~fb^{-1}. To suppress the huge SM background, we confine ourselves to consider the fraction of signal events with two same-sign \tau leptons further decaying into same-sign leptons while the other two \tau leptons decay hadronically. We find that a combination of kinematical selection and machine learning (ML) analysis will yields significant sensitivity to this process at the end of the LHC Run 3.
Text
2503.02432v2
- Author's Original
More information
Accepted/In Press date: 4 March 2025
Additional Information:
22 pages, 9 figures, 7 tables. arXiv admin note: substantial text overlap with arXiv:2401.07289
Keywords:
hep-ph
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Local EPrints ID: 501146
URI: http://eprints.soton.ac.uk/id/eprint/501146
PURE UUID: 5cff65e7-7f0e-4f0a-899d-a519c6ab6a0a
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Date deposited: 27 May 2025 16:50
Last modified: 28 May 2025 01:40
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Author:
Yan Ma
Author:
A. Arhrib
Author:
S. Semlali
Author:
Y. Wang
Author:
Q.S. Yan
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