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CMS-PAS-NPS-25-002
Search for pair production of additional neutral scalars within the Inert Doublet Model in a final state with two electrons or two muons
Abstract: A search for pair production of new scalars predicted by the Inert Doublet Model is performed using proton-proton collisions collected with the CMS detector at the CERN LHC at $ \sqrt{s}= $13 and 13.6 TeV, corresponding to integrated luminosities of 138 and 35 fb$ ^{-1} $, respectively. Within this model, four additional scalar bosons (H, A, H$ ^{+} $, H$ ^{-} $) are predicted, and because of an additional discrete symmetry, the lightest new scalar, H, is stable, rendering it a viable dark matter candidate. The target final state consists of exactly two opposite-charge same-flavour leptons (electrons or muons), with very little hadronic activity, and missing transverse momentum due to the stable neutral scalars. A parameterised neural network is used to separate the signal from the standard model background. No significant excess of events is observed. Exclusion limits at 95% confidence level are set on the production cross section of the two new neutral scalars, H and A, expressed in terms of their masses, $ m_{\mathrm{H}} $ and $ m_{\mathrm{A}} $, in the $ m_{\mathrm{H}} $ vs. $ m_{\mathrm{A}}-m_\mathrm{H} $ plane. The observed (expected) exclusion region reaches $ m_{\mathrm{H}}=108 (106) $ GeV for $ m_{\mathrm{A}}-m_\mathrm{H}=78 (76) $ GeV and at $ m_{\mathrm{H}}= $ 60 GeV, covers the range of $ m_{\mathrm{A}}-m_\mathrm{H}= $35--90 (32--90) GeV.
Figures & Tables Summary References CMS Publications
Figures

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Figure 1:
Leading-order Feynman diagrams of: (left) $ \mathrm{H}\mathrm{H}\ell^+\ell^- $ production through $ \text{A}\mathrm{H} $ production, (middle) $ \mathrm{H}\mathrm{H}\ell^+\ell^-\nu\overline{\nu} $ through $ \text{H}^{\pm} \mathrm{H}^\mp $ production, and (right) $ \mathrm{H}\mathrm{H}\ell^+\ell^- $ through $ \mathrm{h}\mathrm{Z} $ production.

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Figure 1-a:
Leading-order Feynman diagrams of: (left) $ \mathrm{H}\mathrm{H}\ell^+\ell^- $ production through $ \text{A}\mathrm{H} $ production, (middle) $ \mathrm{H}\mathrm{H}\ell^+\ell^-\nu\overline{\nu} $ through $ \text{H}^{\pm} \mathrm{H}^\mp $ production, and (right) $ \mathrm{H}\mathrm{H}\ell^+\ell^- $ through $ \mathrm{h}\mathrm{Z} $ production.

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Figure 1-b:
Leading-order Feynman diagrams of: (left) $ \mathrm{H}\mathrm{H}\ell^+\ell^- $ production through $ \text{A}\mathrm{H} $ production, (middle) $ \mathrm{H}\mathrm{H}\ell^+\ell^-\nu\overline{\nu} $ through $ \text{H}^{\pm} \mathrm{H}^\mp $ production, and (right) $ \mathrm{H}\mathrm{H}\ell^+\ell^- $ through $ \mathrm{h}\mathrm{Z} $ production.

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Figure 1-c:
Leading-order Feynman diagrams of: (left) $ \mathrm{H}\mathrm{H}\ell^+\ell^- $ production through $ \text{A}\mathrm{H} $ production, (middle) $ \mathrm{H}\mathrm{H}\ell^+\ell^-\nu\overline{\nu} $ through $ \text{H}^{\pm} \mathrm{H}^\mp $ production, and (right) $ \mathrm{H}\mathrm{H}\ell^+\ell^- $ through $ \mathrm{h}\mathrm{Z} $ production.

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Figure 2:
Total signal cross section obtained with MadGraph-5_aMC@NLO in the $ (m_{\text{H}}, m_{\text{A}}-m_{\text{H}}) $ plane for Run 2 (left) and Run 3 (right), computed for leptons with transverse momentum $ p_{\mathrm{T}} > $ 10 GeV with a numerical integration error smaller than 0.1%. The simulated training points (circles) are used for training the classifier and for the final statistical inference. The validation points (crosses) are used for testing the interpolation of the signal.

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Figure 2-a:
Total signal cross section obtained with MadGraph-5_aMC@NLO in the $ (m_{\text{H}}, m_{\text{A}}-m_{\text{H}}) $ plane for Run 2 (left) and Run 3 (right), computed for leptons with transverse momentum $ p_{\mathrm{T}} > $ 10 GeV with a numerical integration error smaller than 0.1%. The simulated training points (circles) are used for training the classifier and for the final statistical inference. The validation points (crosses) are used for testing the interpolation of the signal.

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Figure 2-b:
Total signal cross section obtained with MadGraph-5_aMC@NLO in the $ (m_{\text{H}}, m_{\text{A}}-m_{\text{H}}) $ plane for Run 2 (left) and Run 3 (right), computed for leptons with transverse momentum $ p_{\mathrm{T}} > $ 10 GeV with a numerical integration error smaller than 0.1%. The simulated training points (circles) are used for training the classifier and for the final statistical inference. The validation points (crosses) are used for testing the interpolation of the signal.

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Figure 3:
Distributions of key variables after the preselection and OCSF pair selection for 2016--2018 and 2022 combined. All SM processes are modelled with simulation, except the contribution from events with jets misidentified as leptons (MisID) which are calculated using control samples in data, as described in Section 7.4. The combined statistical and experimental uncertainty for the SM prediction is shown as the shaded band. Two representative IDM signal samples are also shown, indicated by IDM($ m_{\text{H}} $,$ m_{\text{A}} $) with the masses given in GeV, and with their normalisation scaled by a factor of 100 for clarity. The first and last bins contain the underflow and overflow events, respectively. The lower panels show the ratio of the data over the SM prediction. The error bars show the statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 3-a:
Distributions of key variables after the preselection and OCSF pair selection for 2016--2018 and 2022 combined. All SM processes are modelled with simulation, except the contribution from events with jets misidentified as leptons (MisID) which are calculated using control samples in data, as described in Section 7.4. The combined statistical and experimental uncertainty for the SM prediction is shown as the shaded band. Two representative IDM signal samples are also shown, indicated by IDM($ m_{\text{H}} $,$ m_{\text{A}} $) with the masses given in GeV, and with their normalisation scaled by a factor of 100 for clarity. The first and last bins contain the underflow and overflow events, respectively. The lower panels show the ratio of the data over the SM prediction. The error bars show the statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 3-b:
Distributions of key variables after the preselection and OCSF pair selection for 2016--2018 and 2022 combined. All SM processes are modelled with simulation, except the contribution from events with jets misidentified as leptons (MisID) which are calculated using control samples in data, as described in Section 7.4. The combined statistical and experimental uncertainty for the SM prediction is shown as the shaded band. Two representative IDM signal samples are also shown, indicated by IDM($ m_{\text{H}} $,$ m_{\text{A}} $) with the masses given in GeV, and with their normalisation scaled by a factor of 100 for clarity. The first and last bins contain the underflow and overflow events, respectively. The lower panels show the ratio of the data over the SM prediction. The error bars show the statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 3-c:
Distributions of key variables after the preselection and OCSF pair selection for 2016--2018 and 2022 combined. All SM processes are modelled with simulation, except the contribution from events with jets misidentified as leptons (MisID) which are calculated using control samples in data, as described in Section 7.4. The combined statistical and experimental uncertainty for the SM prediction is shown as the shaded band. Two representative IDM signal samples are also shown, indicated by IDM($ m_{\text{H}} $,$ m_{\text{A}} $) with the masses given in GeV, and with their normalisation scaled by a factor of 100 for clarity. The first and last bins contain the underflow and overflow events, respectively. The lower panels show the ratio of the data over the SM prediction. The error bars show the statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 3-d:
Distributions of key variables after the preselection and OCSF pair selection for 2016--2018 and 2022 combined. All SM processes are modelled with simulation, except the contribution from events with jets misidentified as leptons (MisID) which are calculated using control samples in data, as described in Section 7.4. The combined statistical and experimental uncertainty for the SM prediction is shown as the shaded band. Two representative IDM signal samples are also shown, indicated by IDM($ m_{\text{H}} $,$ m_{\text{A}} $) with the masses given in GeV, and with their normalisation scaled by a factor of 100 for clarity. The first and last bins contain the underflow and overflow events, respectively. The lower panels show the ratio of the data over the SM prediction. The error bars show the statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 4:
Distributions of the pNN output for the data and the SM expectations in the SR after the background-only fit to the data in the (left) $ \mathrm{e}^-\mathrm{e}^+ $ channel and (right) $ \mu^-\mu^+ $ channel for $ m_{\text{H}} = $ 70 and $ m_{\text{A}} = $ 120 GeV. The combined statistical and experimental uncertainty is shown by the shaded band. The dotted black line represents the signal, referred to as IDM$ (m_{\text{H}},m_{\text{A}}) $, with masses in units of GeV. The lower panels show the ratio of data to the SM expectation. The error bars show statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 4-a:
Distributions of the pNN output for the data and the SM expectations in the SR after the background-only fit to the data in the (left) $ \mathrm{e}^-\mathrm{e}^+ $ channel and (right) $ \mu^-\mu^+ $ channel for $ m_{\text{H}} = $ 70 and $ m_{\text{A}} = $ 120 GeV. The combined statistical and experimental uncertainty is shown by the shaded band. The dotted black line represents the signal, referred to as IDM$ (m_{\text{H}},m_{\text{A}}) $, with masses in units of GeV. The lower panels show the ratio of data to the SM expectation. The error bars show statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 4-b:
Distributions of the pNN output for the data and the SM expectations in the SR after the background-only fit to the data in the (left) $ \mathrm{e}^-\mathrm{e}^+ $ channel and (right) $ \mu^-\mu^+ $ channel for $ m_{\text{H}} = $ 70 and $ m_{\text{A}} = $ 120 GeV. The combined statistical and experimental uncertainty is shown by the shaded band. The dotted black line represents the signal, referred to as IDM$ (m_{\text{H}},m_{\text{A}}) $, with masses in units of GeV. The lower panels show the ratio of data to the SM expectation. The error bars show statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 5:
Distributions of the data and the SM expectations in all CRs after the background-only fit to the data for $ m_{\text{H}} = $ 70 and $ m_{\text{A}} = $ 120 GeV. The dilepton $ p_{\mathrm{T}} $ (left plot) is used as the observable in the fit for the two $ \mathrm{W^-}\mathrm{W^+}/{\mathrm{t}\overline{\mathrm{t}}} $ CRs as well as the different-flavour MisID CR, and the pNN output (right plot) is used for the ZZ CR, both WZ CRs (opposite-charge and same-charge) and the same-charge MisID CRs. The combined statistical and experimental uncertainty is shown by the shaded band. The lower panels show the ratio of data to the SM expectation. The error bars show statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 5-a:
Distributions of the data and the SM expectations in all CRs after the background-only fit to the data for $ m_{\text{H}} = $ 70 and $ m_{\text{A}} = $ 120 GeV. The dilepton $ p_{\mathrm{T}} $ (left plot) is used as the observable in the fit for the two $ \mathrm{W^-}\mathrm{W^+}/{\mathrm{t}\overline{\mathrm{t}}} $ CRs as well as the different-flavour MisID CR, and the pNN output (right plot) is used for the ZZ CR, both WZ CRs (opposite-charge and same-charge) and the same-charge MisID CRs. The combined statistical and experimental uncertainty is shown by the shaded band. The lower panels show the ratio of data to the SM expectation. The error bars show statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 5-b:
Distributions of the data and the SM expectations in all CRs after the background-only fit to the data for $ m_{\text{H}} = $ 70 and $ m_{\text{A}} = $ 120 GeV. The dilepton $ p_{\mathrm{T}} $ (left plot) is used as the observable in the fit for the two $ \mathrm{W^-}\mathrm{W^+}/{\mathrm{t}\overline{\mathrm{t}}} $ CRs as well as the different-flavour MisID CR, and the pNN output (right plot) is used for the ZZ CR, both WZ CRs (opposite-charge and same-charge) and the same-charge MisID CRs. The combined statistical and experimental uncertainty is shown by the shaded band. The lower panels show the ratio of data to the SM expectation. The error bars show statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 6:
Distributions of the pNN output for the data and the SM expectations in the SR after the background-only fit to the data in the (left) $ \mathrm{e}^-\mathrm{e}^+ $ channel and (right) $ \mu^-\mu^+ $ channel for $ m_{\text{H}} = $ 70 and $ m_{\text{A}} = $ 160 GeV. The combined statistical and experimental uncertainty is shown by the shaded band. The dotted black line represents the signal, referred to as IDM$ (m_{\text{H}},m_{\text{A}}) $, with masses in units of GeV. The lower panels show the ratio of data to the SM expectation. The error bars show statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 6-a:
Distributions of the pNN output for the data and the SM expectations in the SR after the background-only fit to the data in the (left) $ \mathrm{e}^-\mathrm{e}^+ $ channel and (right) $ \mu^-\mu^+ $ channel for $ m_{\text{H}} = $ 70 and $ m_{\text{A}} = $ 160 GeV. The combined statistical and experimental uncertainty is shown by the shaded band. The dotted black line represents the signal, referred to as IDM$ (m_{\text{H}},m_{\text{A}}) $, with masses in units of GeV. The lower panels show the ratio of data to the SM expectation. The error bars show statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 6-b:
Distributions of the pNN output for the data and the SM expectations in the SR after the background-only fit to the data in the (left) $ \mathrm{e}^-\mathrm{e}^+ $ channel and (right) $ \mu^-\mu^+ $ channel for $ m_{\text{H}} = $ 70 and $ m_{\text{A}} = $ 160 GeV. The combined statistical and experimental uncertainty is shown by the shaded band. The dotted black line represents the signal, referred to as IDM$ (m_{\text{H}},m_{\text{A}}) $, with masses in units of GeV. The lower panels show the ratio of data to the SM expectation. The error bars show statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 7:
Distributions of the data and the SM expectations in all CRs after the background-only fit to the data for $ m_{\text{H}} = $ 70 and $ m_{\text{A}} = $ 160 GeV. The dilepton $ p_{\mathrm{T}} $ (left plot) is used as the observable in the fit for the two $ \mathrm{W^-}\mathrm{W^+}/{\mathrm{t}\overline{\mathrm{t}}} $ CRs as well as the different-flavour MisID CR, and the pNN output (right plot) is used for the ZZ CR, both WZ CRs (opposite-charge and same-charge) and the same-charge MisID CRs. The combined statistical and experimental uncertainty is shown by the shaded band. The lower panels show the ratio of data to the SM expectation. The error bars show statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 7-a:
Distributions of the data and the SM expectations in all CRs after the background-only fit to the data for $ m_{\text{H}} = $ 70 and $ m_{\text{A}} = $ 160 GeV. The dilepton $ p_{\mathrm{T}} $ (left plot) is used as the observable in the fit for the two $ \mathrm{W^-}\mathrm{W^+}/{\mathrm{t}\overline{\mathrm{t}}} $ CRs as well as the different-flavour MisID CR, and the pNN output (right plot) is used for the ZZ CR, both WZ CRs (opposite-charge and same-charge) and the same-charge MisID CRs. The combined statistical and experimental uncertainty is shown by the shaded band. The lower panels show the ratio of data to the SM expectation. The error bars show statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 7-b:
Distributions of the data and the SM expectations in all CRs after the background-only fit to the data for $ m_{\text{H}} = $ 70 and $ m_{\text{A}} = $ 160 GeV. The dilepton $ p_{\mathrm{T}} $ (left plot) is used as the observable in the fit for the two $ \mathrm{W^-}\mathrm{W^+}/{\mathrm{t}\overline{\mathrm{t}}} $ CRs as well as the different-flavour MisID CR, and the pNN output (right plot) is used for the ZZ CR, both WZ CRs (opposite-charge and same-charge) and the same-charge MisID CRs. The combined statistical and experimental uncertainty is shown by the shaded band. The lower panels show the ratio of data to the SM expectation. The error bars show statistical uncertainties, while the hashed bands include both statistical and systematic components.

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Figure 8:
The 95% CL exclusion limits in terms of $ m_{\text{H}} $ and $ m_{\text{A}} - m_{\text{H}} $. The red dashed lines indicate the $ \pm $ 1 standard deviation bands from experimental uncertainties, whilst the black dashed lines indicate the $ \pm $ 1 standard deviation bands from theory uncertainties in the signal samples. The exclusion limits from LEP reinterpretation and relic density constraints are overlaid in green and yellow, respectively [7]. Limits are calculated with the IDM parameters $ m_{\text{H}^{\pm}} = m_{\text{A}} + $ 50 GeV, $ \lambda_2 = $ 1, and $ \lambda_{345} = 10^{-6} $. The limits, however, are insensitive to the choice of the $ \lambda_2 $ value, and to changes in the $ m_{\text{H}^{\pm}} $ and $ \lambda_{345} $ within their allowed values.
Tables

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Table 1:
Kinematic preselection of dilepton pairs. All events require at least one dilepton pair passing the "Dilepton" quantities, but with no constraints on the charges or flavours. The $ p_{\mathrm{T}} $ of the leading lepton is dictated by the year-specific thresholds of the single-lepton triggers.

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Table 2:
Signal region selection. Events must also pass the preselection outlined in Table 1. A veto is applied on any event with additional loose leptons.

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Table 3:
Selections for the control regions. For all events, we require at least one dilepton pair passing the preselection outlined in Table 1, as well as any additional requirements given in this table. The second dilepton in the ZZ CR does not have to pass the selection in Table 1. All regions have a veto on any additional loose leptons. The fit variable refers to the fit procedure described in Section 9.

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Table 4:
Uncertainty breakdown in the fitted signal strength for two signal mass points. The sources of uncertainty are separated into different groups.
Summary
The Inert Doublet Model predicts additional scalars, including two neutral scalars H and $ \text{A} $, which couple only to bosons. The lightest neutral scalar, H, is stable and provides a viable dark matter candidate. The pair production of such new scalars is investigated in a final state containing two electrons or two muons. The search is performed using proton-proton collisions at $\sqrt{s}$ 13 (13.6) TeV, delivered by the LHC and recorded by the CMS experiment between 2016 and 2018 (in 2022), with a total integrated luminosity of 138 fb$ ^{-1} $ \ (35 fb$ ^{-1} $ ). After a preselection to remove the largest standard model backgrounds, a parameterised neural network is trained to discriminate the different signal mass points from the remaining backgrounds. Control regions tailored to each of the dominant backgrounds are constructed and used in a simultaneous fit together with the signal region to set 95% confidence level exclusion limits on the signal production cross section in the $ m_{\text{H}} $ vs $ m_{\text{A}}-m_\text{H} $ plane. The observed (expected) exclusion region reaches $ m_{\text{H}}=108 (106) \text{GeV} $ for $ m_{\text{A}}-m_\text{H}=78 (76) \text{GeV} $ and, at $ m_{\text{H}}= $ 60 GeV, covers the range of $ m_{\text{A}}-m_\text{H}= $35--90 (32--90) GeV. These exclusion limits significantly extend the constraints from previous direct and indirect measurements and dark-matter searches. These results represent the first limits on the masses of the neutral scalars in the Inert Doublet Model obtained by a dedicated search using collision data.
References
1 Particle Data Group Collaboration Review of particle physics PRD 110 (2024) 030001
2 L. Roszkowski, E. M. Sessolo, and S. Trojanowski WIMP dark matter candidates and searches\textemdashcurrent status and future prospects Rept. Prog. Phys. 81 (2018) 066201 1707.06277
3 N. G. Deshpande and E. Ma Pattern of Symmetry Breaking with Two Higgs Doublets PRD 18 (1978) 2574
4 R. Barbieri, L. J. Hall, and V. S. Rychkov Improved naturalness with a heavy Higgs: An Alternative road to LHC physics PRD 74 (2006) 015007 hep-ph/0603188
5 Q.-H. Cao, E. Ma, and G. Rajasekaran Observing the Dark Scalar Doublet and its Impact on the Standard-Model Higgs Boson at Colliders PRD 76 (2007) 095011 0708.2939
6 J. Kalinowski, T. Robens, D. Sokolowska, and A. F. Zarnecki IDM Benchmarks for the LHC and Future Colliders Symmetry 13 (2021) 991 2012.14818
7 A. Ilnicka, M. Krawczyk, and T. Robens Inert Doublet Model in light of LHC Run 1 and astrophysical data PRD 93 (2016) 055026 1508.01671
8 J. Kalinowski et al. Exploring Inert Scalars at CLIC JHEP 07 (2019) 053 1811.06952
9 A. F. Zarnecki et al. Searching Inert Scalars at Future e$ ^+ $e$ ^- $ Colliders in Proc. Int. Workshop on Future Linear Colliders, 2020 2002.11716
10 A. Bal et al. Search for additional scalar bosons within the Inert Doublet Model in a final state with two leptons at the FCC-ee Eur. Phys. J. C. 85 (2025) 891 2504.12178
11 J. Braathen, M. Gabelmann, T. Robens, and P. Stylianou Probing the Inert Doublet Model via vector-boson fusion at a muon collider JHEP 05 (2025) 055 2411.13729
12 CLIC Collaboration Pair-production of the charged IDM scalars at high energy CLIC EPJC 82 (2022) 738 2201.07146
13 A. Belyaev et al. Decoding dark matter at future e+e- colliders PRD 106 (2022) 015016 2112.15090
14 Y. Guo-He et al. Searches for dark matter via charged Higgs pair production in the Inert Doublet Model at a $\gamma\gamma$ collider Chin. Phys. C 45 (2021) 103101 2006.06216
15 J. Kalinowski et al. Benchmarking the Inert Doublet Model for $ e^+ e^- $ colliders JHEP 12 (2018) 081 1809.07712
16 A. Datta, N. Ganguly, N. Khan, and S. Rakshit Exploring collider signatures of the inert Higgs doublet model PRD 95 (2017) 015017 1610.00648
17 S. Sekmen Supersymmetry Searches in CMS Run 2: A Complete Review HiHEP 1 (2025) 18 2510.17971
18 D. Dercks and T. Robens Constraining the Inert Doublet Model using Vector Boson Fusion EPJC 79 (2019) 924 1812.07913
19 A. Belyaev et al. Multilepton signatures from dark matter at the LHC JHEP 09 (2022) 173 2204.06411
20 G. B é langer et al. Dilepton constraints in the Inert Doublet Model from Run 1 of the LHC PRD 91 (2015) 115011 1503.07367
21 J. Lahiri, T. Robens, and K. Rolbiecki Constraining the Inert Doublet Model at the LHC 2511.23133
22 I. F. Ginzburg, K. A. Kanishev, M. Krawczyk, and D. Sokolowska Evolution of Universe to the present inert phase PRD 82 (2010) 123533 1009.4593
23 A. Belyaev et al. Advancing LHC probes of dark matter from the inert two-Higgs-doublet model with the monojet signal PRD 99 (2019) 015011 1809.00933
24 A. Belyaev et al. Anatomy of the Inert Two Higgs Doublet Model in the light of the LHC and non-LHC Dark Matter Searches PRD 97 (2018) 035011 1612.00511
25 A. Ilnicka, T. Robens, and T. Stefaniak Constraining Extended Scalar Sectors at the LHC and beyond Mod. Phys. Lett. A 33 (2018) 1830007 1803.03594
26 A. Ilnicka, M. Krawczyk, and T. Robens Constraining the Inert Doublet Model in Proc. 2nd Toyama Int. Workshop on Higgs as a Probe of New Physics, 2015 1505.04734
27 D. Eriksson, J. Rathsman, and O. Stal 2HDMC: Two-Higgs-Doublet Model Calculator Comput. Phys. Commun. 181 (2010) 833 0902.0851
28 G. Altarelli and R. Barbieri Vacuum polarization effects of new physics on electroweak processes PLB 253 (1991) 161
29 M. E. Peskin and T. Takeuchi A New constraint on a strongly interacting Higgs sector PRL 65 (1990) 964
30 M. E. Peskin and T. Takeuchi Estimation of oblique electroweak corrections PRD 46 (1992) 381
31 I. Maksymyk, C. P. Burgess, and D. London Beyond S, T and U PRD 50 (1994) 529 hep-ph/9306267
32 ATLAS Collaboration Combination of searches for invisible decays of the Higgs boson using 139 fb$ ^{-1} $ of proton-proton collision data at $ \sqrt{s}= $ 13 TeV collected with the ATLAS experiment PLB 842 (2023) 137963 2301.10731
33 G. B é langer et al. micrOMEGAs5.0: Freeze-in Comput. Phys. Commun. 231 (2018) 173 1801.03509
34 Planck Collaboration Planck 2018 results. VI. Cosmological parameters Erratum: doi:10./0004-6361/33910e
Astron. Astrophys. 641 (2020) A6
1807.06209
35 LZ Collaboration Dark Matter Search Results from 4.2 $ \text{ }\text{ }\text{Tonne}\text{-}\text{Years} $ of Exposure of the LUX-ZEPLIN (LZ) Experiment PRL 135 (2025) 011802 2410.17036
36 E. Lundstrom, M. Gustafsson, and J. Edsjo The Inert Doublet Model and LEP II Limits PRD 79 (2009) 035013 0810.3924
37 CMS Collaboration The CMS trigger system JINST 12 (2017) P01020 CMS-TRG-12-001
1609.02366
38 CMS Collaboration Performance of the CMS high-level trigger during LHC Run 2 JINST 19 (2024) P11021 CMS-TRG-19-001
2410.17038
39 CMS Collaboration Performance of the CMS Level-1 trigger in proton-proton collisions at $ \sqrt{s} = $ 13 TeV JINST 15 (2020) P10017 CMS-TRG-17-001
2006.10165
40 CMS Collaboration The CMS experiment at the CERN LHC JINST 3 (2008) S08004 1003.4038
41 CMS Collaboration Development of the CMS detector for the CERN LHC Run 3 JINST 19 (2024) P05064 CMS-PRF-21-001
2309.05466
42 J. Alwall et al. The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations JHEP 07 (2014) 079 1405.0301
43 A. Goudelis, B. Herrmann, and O. St\r a l Dark matter in the Inert Doublet Model after the discovery of a Higgs-like boson at the LHC JHEP 09 (2013) 106 1303.3010
44 J. Alwall et al. Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions EPJC 53 (2008) 473 0706.2569
45 Y. Li and F. Petriello Combining QCD and electroweak corrections to dilepton production in FEWZ PRD 86 (2012) 094034 1208.5967
46 R. Frederix and S. Frixione Merging meets matching in MC@NLO JHEP 12 (2012) 061 1209.6215
47 P. Nason A New method for combining NLO QCD with shower Monte Carlo algorithms JHEP 11 (2004) 040 hep-ph/0409146
48 S. Frixione, P. Nason, and C. Oleari Matching NLO QCD computations with Parton Shower simulations: the POWHEG method JHEP 11 (2007) 070 0709.2092
49 S. Alioli, P. Nason, C. Oleari, and E. Re A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX JHEP 06 (2010) 043 1002.2581
50 T. Melia, P. Nason, R. Rontsch, and G. Zanderighi $ {\mathrm{W}^{+}\mathrm{W}^{-}} $, $ {\mathrm{W}\mathrm{Z}} $ and $ {\mathrm{Z}\mathrm{Z}} $ production in the POWHEG BOX JHEP 11 (2011) 078 1107.5051
51 J. M. Campbell and R. K. Ellis MCFM for the Tevatron and the LHC Nucl. Phys. B Proc. Suppl. 205-206 10, 2010
link
1007.3492
52 J. M. Campbell, R. K. Ellis, P. Nason, and E. Re Top-pair production and decay at NLO matched with parton showers JHEP 04 (2015) 114 1412.1828
53 S. Alioli, P. Nason, C. Oleari, and E. Re NLO single-top production matched with shower in POWHEG: \textits- and \textitt-channel contributions Erratum: doi:10./JHEP02()011
JHEP 09 (2009) 111
0907.4076
54 E. Re Single-top $ {\mathrm{W}} $t-channel production matched with parton showers using the POWHEG method EPJC 71 (2011) 1547 1009.2450
55 M. Czakon and A. Mitov Top++: A Program for the Calculation of the Top-Pair Cross-Section at Hadron Colliders Comput. Phys. Commun. 185 (2014) 2930 1112.5675
56 J. Campbell, T. Neumann, and Z. Sullivan Single-top-quark production in the $ t $-channel at NNLO JHEP 02 (2021) 040 2012.01574
57 N. Kidonakis and N. Yamanaka Higher-order corrections for $ tW $ production at high-energy hadron colliders JHEP 05 (2021) 278 2102.11300
58 NNPDF Collaboration Parton distributions for the LHC Run II JHEP 04 (2015) 040 1410.8849
59 T. Sjöstrand et al. An introduction to PYTHIA 8.2 Comput. Phys. Commun. 191 (2015) 159 1410.3012
60 C. Bierlich et al. A comprehensive guide to the physics and usage of PYTHIA 8.3 SciPost Phys. Codeb. 8 (2022) 2203.11601
61 CMS Collaboration Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements EPJC 80 (2020) 4 CMS-GEN-17-001
1903.12179
62 GEANT4 Collaboration GEANT 4 --- a simulation toolkit NIM A 506 (2003) 250
63 CMS Collaboration Particle-flow reconstruction and global event description with the CMS detector JINST 12 (2017) P10003 CMS-PRF-14-001
1706.04965
64 CMS Collaboration Technical proposal for the Phase-II upgrade of the Compact Muon Solenoid CMS Technical Proposal CERN-LHCC-2015-010, CMS-TDR-15-02, 2015
CDS
65 CMS Collaboration Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC JINST 16 (2021) P05014 CMS-EGM-17-001
2012.06888
66 CMS Collaboration Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $ \sqrt{s}= $ 13 TeV JINST 13 (2018) P06015 CMS-MUO-16-001
1804.04528
67 M. Cacciari, G. P. Salam, and G. Soyez The anti--$ k_{\mathrm{T}} $ jet clustering algorithm JHEP 04 (2008) 063 0802.1189
68 M. Cacciari, G. P. Salam, and G. Soyez FastJet User Manual EPJC 72 (2012) 1896 1111.6097
69 CMS Collaboration Pileup mitigation at CMS in 13 TeV data JINST 15 (2020) P09018 CMS-JME-18-001
2003.00503
70 D. Bertolini, P. Harris, M. Low, and N. Tran Pileup Per Particle Identification JHEP 10 (2014) 059 1407.6013
71 CMS Collaboration Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV JINST 12 (2017) P02014 CMS-JME-13-004
1607.03663
72 CMS Collaboration Performance of missing transverse momentum reconstruction in proton-proton collisions at $ \sqrt{s} = $ 13 TeV using the CMS detector JINST 14 (2019) P07004 CMS-JME-17-001
1903.06078
73 E. Bols et al. Jet Flavour Classification Using DeepJet JINST 15 (2020) P12012 2008.10519
74 P. Baldi et al. Parameterized neural networks for high-energy physics EPJC 76 (2016) 235 1601.07913
75 C. G. Lester and A. J. Barr mTGen: mass scale measurements in pair-production at colliders JHEP 12 (2007) 102
76 C. G. Lester The stransverse mass, mt2, in special cases JHEP 05 (2011) 076 1103.5682
77 R. Mahbubani, K. T. Matchev, and M. Park Re-interpreting the oxbridge stransverse mass variable mt2 in general cases JHEP 03 (2013) 134 1212.1720
78 CMS Collaboration Search for Physics Beyond the Standard Model in Events with a $ Z $ Boson, Jets, and Missing Transverse Energy in $ pp $ Collisions at $ \sqrt{s}= $ 7 TeV PLB 716 (2012) 260 CMS-SUS-11-021
1204.3774
79 T. Chen and C. Guestrin XGBoost: A Scalable Tree Boosting System in ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2016
Proc. 2 (2016) 785
1603.02754
80 A. Paszke et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library 1912.01703
81 D.-A. Clevert, T. Unterthiner, and S. Hochreiter Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) link 1511.07289
82 D. Kingma and J. Ba Adam: A Method for Stochastic Optimization Proc. Int. Conf. on Learning Representations. 201 (1900) 4 1412.6980
83 J. Terven et al. A comprehensive survey of loss functions and metrics in deep learning Artif. Intell. Rev. 58 (2025) 195
84 S. Rippa An algorithm for selecting a good parameter c in radial basis function interpolation Adv. Comput. Math. 11 (1999) 193
85 CMS Collaboration Search for physics beyond the standard model in dilepton mass spectra in proton-proton collisions at $ \sqrt{s}= $ 8 TeV JHEP 04 (2015) 025 CMS-EXO-12-061
1412.6302
86 CMS Collaboration Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV JINST 13 (2018) P05011 CMS-BTV-16-002
1712.07158
87 CMS Collaboration Measurement of the inelastic proton-proton cross section at $ \sqrt{s}= $ 13 TeV JHEP 07 (2018) 161 CMS-FSQ-15-005
1802.02613
88 C. Collaboration Precision luminosity measurement in proton-proton collisions at $ \sqrt{s} = $ 13 tev in 2015 and 2016 at cms EPJC 81 (2021) 800 2104.01927
89 CMS Collaboration CMS luminosity measurement for the 2017 data-taking period at $ \sqrt{s} = $ 13 TeV CMS Physics Analysis Summary, 2018
CMS-PAS-LUM-17-004
CMS-PAS-LUM-17-004
90 CMS Collaboration CMS luminosity measurement for the 2018 data-taking period at $ \sqrt{s} = $ 13 TeV CMS Physics Analysis Summary, 2019
CMS-PAS-LUM-18-002
CMS-PAS-LUM-18-002
91 CMS Collaboration Luminosity measurement in proton-proton collisions at 13.6 TeV in 2022 at CMS CMS Physics Analysis Summary, CERN, 2024
CMS-PAS-LUM-22-001
CMS-PAS-LUM-22-001
92 J. Butterworth et al. PDF4LHC recommendations for LHC Run II JPG 43 (2016) 023001 1510.03865
93 R. D. Ball et al. The PDF4LHC21 combination of global PDF fits for the LHC Run III* JPG 49 (2022) 080501 2203.05506
94 R. Barlow and C. Beeston Fitting using finite Monte Carlo samples Comput. Phys. Commun. 77 (1993) 219
95 CMS Collaboration The CMS Statistical Analysis and Combination Tool: Combine Comput. Softw. Big Sci. 8 (2024) 19 CMS-CAT-23-001
2404.06614
96 G. Cowan, K. Cranmer, E. Gross, and O. Vitells Asymptotic formulae for likelihood-based tests of new physics Erratum: doi:10.1140/epjc/s2-013-2501-z
EPJC 71 (2011) 1554
1007.1727
97 T. Junk Confidence level computation for combining searches with small statistics NIM A 434 (1999) 435 hep-ex/9902006
98 A. L. Read Presentation of search results: the CLs technique JPG 28 (2002) 2693
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