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Paper Citation Record · LEDGER

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

As of 11 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 0 inbound Pith citation observations for arXiv:2608.06957.

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pith.paper-citation-record.v1
2608.06957 v1

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Outbound references

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

Reference 1

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Observation f29e982a-d558-48a0-82e4-dddbe894b03c · outbound

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

Reference 2

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Observation 7b3c7842-f01e-4e14-987a-a497c9c5163a · outbound

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

Reference 3

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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

Reference 4

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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 5

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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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Observation 9aa3b51f-41f5-4983-bd79-582f52717c3d · outbound

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

Reference 7

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Observation 6c4323eb-f10f-4d1a-803c-0eb6cfd4d947 · outbound

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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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

Reference 9

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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

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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

Reference 14

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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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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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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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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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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

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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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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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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

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

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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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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

Reference 30

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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

Reference 32

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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

Reference 35

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Observation 8f970d65-cd47-4798-816c-46706481cb9f · outbound

This paper cites One-loop radiative corrections to $e^+ e^-\to Zh^0/H^0A^0$ in the Inert Higgs Doublet Model.

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

Reference 36

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Observation 31114962-4623-4924-a5f5-fb27265a38c7 · outbound

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

Reference 37

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This paper cites One-loop QED and Weak Corrections to $\gamma \gamma \to H^\pm H^\mp$ in the Inert Doublet Model.

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

Reference 38

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Observation a87cfed3-4c61-496c-b985-fb99ece5c4ff · outbound

This paper cites Apollinari, I.

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

Reference 39

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Observation c53da58a-f0a4-4c98-b65a-674447cbb91b · outbound

This paper cites Abada et al.

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

Reference 40

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Observation 30af334c-c0ad-40f6-9ebe-afd22dd300ce · outbound

This paper cites Accettura et al.

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

Reference 41

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This paper cites Revisiting Inert Doublet Model Parameters.

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

Reference 42

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Observation 473e226a-3642-423b-8c18-83be40e9a4f1 · outbound

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

Reference 43

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Observation 85622294-e23a-4e35-bd34-3397929d0ec2 · outbound

This paper cites Leading two-loop corrections to the Higgs di-photon decay in the Inert Doublet Model.

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

Reference 44

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Observation 127c20eb-0047-45b3-8cd3-5db25280802f · outbound

This paper cites Evolution of Universe to the present inert phase.

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

Reference 45

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Observation a14d7bb3-73ab-4477-aa44-624f61c7b8bd · outbound

This paper cites Mass bounds of the lightest CP-even Higgs boson in the two-Higgs-doublet model.

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

Reference 46

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Observation 39a3992b-9133-4869-b2a9-be1e50f94726 · outbound

This paper cites Lee-Quigg-Thacker Bounds for Higgs Boson Masses in a Two-Doublet Model.

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

Reference 47

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Observation d0fff35e-e2a5-406f-ba90-51b535502313 · outbound

This paper cites Note on tree-level unitarity in the General Two Higgs Doublet Model.

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

Reference 48

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Observation eb977270-2656-4ff2-9138-e6e7eec081bd · outbound

This paper cites Inert Doublet Model and LEP II Limits.

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

Reference 49

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Observation 94e0d538-8ac3-43ff-ae66-72dc6c59e851 · outbound

This paper cites Diphoton rate in the Inert Doublet Model with a 125 GeV Higgs boson.

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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Observation 75706278-78b9-47b5-86f8-1871c9e6699e · outbound

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

Reference 51

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Observation 4bac1bd1-eda5-47ac-afcf-1ed50ee2ce75 · outbound

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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Observation 27d0fd6e-c00d-419d-a09c-d4dcabb084fe · outbound

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

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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

This paper cites an unresolved cited work.

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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