Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis | Paper Citation Record

Observation b91e0da2-a8fa-4f65-b31d-db306edf3328 · outbound

This paper cites Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC

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This paper cites Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC

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This paper cites Planck 2018 results. I. Overview and the cosmological legacy of Planck.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Planck 2018 results. I. Overview and the cosmological legacy of Planck

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This paper cites Planck 2018 results. VI. Cosmological parameters.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Planck 2018 results. VI. Cosmological parameters

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Unresolved cited work

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This paper cites Ma, Phys.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Ma, Phys

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This paper cites Precise probing of the inert Higgs-doublet model at the LHC.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Precise probing of the inert Higgs-doublet model at the LHC

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This paper cites Belyaev, G.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Belyaev, G

Reference 8

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Observation 7e7c608a-5448-4b8a-b37e-381f470dfc15 · outbound

This paper cites Constraining the Inert Doublet Model using Vector Boson Fusion.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Constraining the Inert Doublet Model using Vector Boson Fusion

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This paper cites Vector Boson Fusion in the Inert Doublet Model.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Vector Boson Fusion in the Inert Doublet Model

Reference 10

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Observation a096e37b-c9b4-4081-abd8-5979f0d45622 · outbound

This paper cites Constraints to Dark Matter from Inert Higgs Doublet Model.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Constraints to Dark Matter from Inert Higgs Doublet Model

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Unresolved cited work

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This paper cites Exploring the Inert Doublet Model through the dijet plus missing transverse energy channel at the LHC.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Exploring the Inert Doublet Model through the dijet plus missing transverse energy channel at the LHC

Reference 13

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This paper cites Exploring collider signatures of the inert Higgs doublet model.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Exploring collider signatures of the inert Higgs doublet model

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This paper cites Status of the Inert Doublet Model and the Role of multileptons at the LHC.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Status of the Inert Doublet Model and the Role of multileptons at the LHC

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This paper cites Higgs-strahlung at the LHC in the inert doublet model.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Higgs-strahlung at the LHC in the inert doublet model

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This paper cites Inert Higgs Dark Matter for CDF-II W-boson Mass and Detection Prospects.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Inert Higgs Dark Matter for CDF-II W-boson Mass and Detection Prospects

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This paper cites Pair production of charged IDM scalars at high energy CLIC.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Pair production of charged IDM scalars at high energy CLIC

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This paper cites Pair-production of the charged IDM scalars at high energy CLIC.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Pair-production of the charged IDM scalars at high energy CLIC

Reference 19

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This paper cites Benchmarking the Inert Doublet Model for e+ e- colliders.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Benchmarking the Inert Doublet Model for e+ e- colliders

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This paper cites Production of Inert Scalars at the high energy $e^+ e^-$ colliders.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Production of Inert Scalars at the high energy $e^+ e^-$ colliders

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This paper cites Reconstruction of Inert Doublet Scalars at the International Linear Collider.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Reconstruction of Inert Doublet Scalars at the International Linear Collider

Reference 22

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Observation 438e1bfd-b4f1-42eb-8975-c8b40a941fa0 · outbound

This paper cites Ouhammou, M.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Ouhammou, M

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This paper cites Searches for dark matter via charged Higgs pair production in the Inert Doublet Model at $\gamma\gamma$ collider.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Searches for dark matter via charged Higgs pair production in the Inert Doublet Model at $\gamma\gamma$ collider

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Probing the Inert Doublet Model via Vector-Boson Fusion at a Muon Collider

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Dark matter in the Inert Doublet Model after the discovery of a Higgs-like boson at the LHC

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This paper cites Arina, J.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Arina, J

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Gustafsson, E

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Positrons and antiprotons from inert doublet model dark matter

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Electroweak corrections to the direct detection cross section of inert higgs dark matter

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Probing the inert doublet model using jet substructure with a multivariate analysis

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis The inert doublet model in the light of Fermi-LAT gamma-ray data: a global fit analysis

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Radiative corrections to the Triple Higgs Coupling in the Inert Higgs Doublet Model

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Associated Production of Higgs at Linear Collider in the Inert Higgs Doublet Model

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Unresolved cited work

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis One-loop radiative corrections to $e^+ e^-\to Zh^0/H^0A^0$ in the Inert Higgs Doublet Model

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This paper cites Abouabid, A.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Abouabid, A

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis One-loop QED and Weak Corrections to $\gamma \gamma \to H^\pm H^\mp$ in the Inert Doublet Model

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Apollinari, I

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Abada et al

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Accettura et al

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Revisiting Inert Doublet Model Parameters

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Abouabid, A

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Leading two-loop corrections to the Higgs di-photon decay in the Inert Doublet Model

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Evolution of Universe to the present inert phase

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Mass bounds of the lightest CP-even Higgs boson in the two-Higgs-doublet model

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Lee-Quigg-Thacker Bounds for Higgs Boson Masses in a Two-Doublet Model

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Note on tree-level unitarity in the General Two Higgs Doublet Model

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Inert Doublet Model and LEP II Limits

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Diphoton rate in the Inert Doublet Model with a 125 GeV Higgs boson

Reference 50

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This paper cites h\to \gamma \gamma In Inert Higgs Doublet Model.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis h\to \gamma \gamma In Inert Higgs Doublet Model

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This paper cites Natural Dark Matter from an Unnatural Higgs Boson and New Colored Particles at the TeV Scale.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Natural Dark Matter from an Unnatural Higgs Boson and New Colored Particles at the TeV Scale

Reference 52

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This paper cites Two-loop corrections to the $\rho$ parameter in Two-Higgs-Doublet Models.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Two-loop corrections to the $\rho$ parameter in Two-Higgs-Doublet Models

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-10T17:47:54.084174Z

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Observation 23033e78-8542-4e8b-b30d-ad1393890913 · outbound

This paper cites 2HDMC - Two-Higgs-Doublet Model Calculator.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis 2HDMC - Two-Higgs-Doublet Model Calculator

Reference 54

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Observation 49c1592b-4fda-4f53-b1e1-6f6b132d78f2 · outbound

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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Unresolved cited work

Reference 55

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Observation 5943a382-01ba-4ab8-bc86-8465f46d2230 · outbound

This paper cites anyH3: precise predictions for the trilinear Higgs coupling in the Standard Model and beyond.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis anyH3: precise predictions for the trilinear Higgs coupling in the Standard Model and beyond

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T17:47:53.221694Z

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Observation af344ef1-aa45-43c7-ab63-ce7725731175 · outbound

This paper cites Aad et al.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Aad et al

Reference 57

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Observation db8942af-3962-4f70-b184-c0f57a294fc5 · outbound

This paper cites Hayrapetyan et al.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Hayrapetyan et al

Reference 58

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Observation 9b5827a8-2b26-468a-b9ae-69de428c59fa · outbound

This paper cites Combination of searches for Higgs boson pair production in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Combination of searches for Higgs boson pair production in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-10T17:47:53.232128Z

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Observation fc8d777a-cfe6-4358-a603-f591ac5eb8a4 · outbound

This paper cites Aad et al.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Aad et al

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-10T17:47:53.235802Z

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Observation 49570f6a-78cc-4f6d-9d32-3c79f60e678b · outbound

This paper cites Relic density of dark matter in the inert doublet model beyond leading order for the low mass region: 1. Renormalisation and constraints.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Relic density of dark matter in the inert doublet model beyond leading order for the low mass region: 1. Renormalisation and constraints

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-08-10T17:47:53.649854Z

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Observation 7b1dedc5-eb92-4610-a4e2-3d224a81d25f · outbound

This paper cites Belanger, F.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Belanger, F

Reference 62

Resolution: verified exact; raw_fallback, observed 2026-08-10T17:47:53.634998Z

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Observation f4b31975-5d01-4c74-9761-2192f9974c84 · outbound

This paper cites Belanger, F.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Belanger, F

Reference 63

Resolution: verified exact; doi, observed 2026-08-10T17:47:53.348902Z

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Observation 2e705b1c-a72d-48ab-b6cc-6fcf9320c45d · outbound

This paper cites Belanger, F.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Belanger, F

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T17:47:53.248750Z

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Observation 331d366f-ad77-4407-bd08-c6cbed09cc53 · outbound

This paper cites micrOMEGAs3.1 : a program for calculating dark matter observables.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis micrOMEGAs3.1 : a program for calculating dark matter observables

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-10T17:47:53.252154Z

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Observation 75010adb-f460-4253-bbb0-1e55adb992c2 · outbound

This paper cites micrOMEGAs5.0 : freeze-in.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis micrOMEGAs5.0 : freeze-in

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-10T17:47:53.255590Z

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Unavailable: canonical work link unavailable.

Observation ab073974-90ba-49a6-bb28-9fed045ddc8d · outbound

This paper cites Banerjee, F.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Banerjee, F

Reference 67

Resolution: verified exact; doi, observed 2026-08-10T17:47:53.329815Z

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Observation 827fb54a-193b-4564-b126-7d8c914564ae · outbound

This paper cites First Dark Matter Search Results from the LUX-ZEPLIN (LZ) Experiment.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis First Dark Matter Search Results from the LUX-ZEPLIN (LZ) Experiment

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-10T17:47:53.262502Z

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Observation b6f2fea3-7eff-4ae8-9ed1-e2e72c3cd606 · outbound

This paper cites The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T17:47:53.266902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d99077a8-c3e3-4c73-a4db-3ca19d242833 · outbound

This paper cites Navas et al.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Navas et al

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-10T17:47:53.270727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation baac30c8-365c-4cff-be64-e7ebf24cf23b · outbound

This paper cites XGBoost: A Scalable Tree Boosting System.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis XGBoost: A Scalable Tree Boosting System

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-10T17:47:53.274128Z

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Unavailable: canonical work link unavailable.

Observation 6eadcb1a-f840-49d5-a76e-0cf6c7d175d5 · outbound

This paper cites Shapley, RAND Corporation, Santa Monica, CA, U.S.A.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Shapley, RAND Corporation, Santa Monica, CA, U.S.A

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:47:54.891414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 326221cd-4912-497e-ab26-f78cc35d31bb · outbound

This paper cites Resurrecting $b\bar{b}h$ with kinematic shapes.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Resurrecting $b\bar{b}h$ with kinematic shapes

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-08-10T17:47:53.463934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1fffcca3-aa1b-4a0f-9a52-38ce62059ae8 · outbound

This paper cites Machine learning the trilinear and light-quark Yukawa couplings from Higgs pair kinematic shapes.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Machine learning the trilinear and light-quark Yukawa couplings from Higgs pair kinematic shapes

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-08-10T17:47:53.448534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9d80da26-5cee-4f06-990f-5cc7792b0746 · outbound

This paper cites Interpretable machine learning in Physics.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Interpretable machine learning in Physics

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-10T17:47:53.289827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba327882-ec70-49b1-8316-f860d21d2c80 · outbound

This paper cites Classifying the CP properties of the ggH coupling in H+2j production.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Classifying the CP properties of the ggH coupling in H+2j production

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-10T17:47:53.293479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 966eb20a-9b1e-4854-b164-ea18baac0ff5 · outbound

This paper cites Lundberg et al., Nature Machine Intel.

Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis Lundberg et al., Nature Machine Intel

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:47:54.880342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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