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Paper Citation Record · LEDGER

Machine Learning in the 2HDM2S model for Dark Matter

As of 5 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 2 inbound Pith citation observations for arXiv:2509.01677.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.01677 v4

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T19:21:02.625794Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T12:51:49.155762Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-02T10:26:52.254140Z

Reference resolution

62 of 62 outbound references displayed

  • verified exact56
  • verified fuzzy4
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8a2f8b72-b22c-49cd-8a85-5134e065765e · outbound

This paper cites Planck 2013 results. I. Overview of products and scientific results.

Machine Learning in the 2HDM2S model for Dark Matter Planck 2013 results. I. Overview of products and scientific results

Reference 1

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local_arxiv, observed 2026-05-18T19:21:47.531865Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 6369e422-df58-4689-9c9d-7f4ab2a93b18 · outbound

This paper cites Theory and phenomenology of two-Higgs-doublet models.

Machine Learning in the 2HDM2S model for Dark Matter Theory and phenomenology of two-Higgs-doublet models

Reference 2

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local_arxiv, observed 2026-05-18T19:21:47.515177Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 66b354f6-cc16-45e3-8042-4dd424394c4f · outbound

This paper cites The Anatomy of Electro-Weak Symmetry Breaking. II: The Higgs bosons in the Minimal Supersymmetric Model.

Machine Learning in the 2HDM2S model for Dark Matter The Anatomy of Electro-Weak Symmetry Breaking. II: The Higgs bosons in the Minimal Supersymmetric Model

Reference 3

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local_arxiv, observed 2026-05-18T19:21:47.491536Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation f5576c2e-cc59-46c9-a750-856363f45664 · outbound

This paper cites an unresolved cited work.

Machine Learning in the 2HDM2S model for Dark Matter Unresolved cited work

Reference 4

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raw_fallback, observed 2026-05-18T19:22:50.079545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation de6ee86c-bf48-4efe-901c-12cb7531f059 · outbound

This paper cites Dimopoulos, D.

Machine Learning in the 2HDM2S model for Dark Matter Dimopoulos, D

Reference 5

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raw_fallback, observed 2026-05-18T19:22:50.076557Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 27fe1a41-b243-49ef-9f81-e02c43663d13 · outbound

This paper cites Turning off the Lights: How Dark is Dark Matter?.

Machine Learning in the 2HDM2S model for Dark Matter Turning off the Lights: How Dark is Dark Matter?

Reference 6

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local_arxiv, observed 2026-05-18T19:21:47.537255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation f993eea2-d63d-4d8a-90e1-29ac4dd6e308 · outbound

This paper cites Particle Dark Matter: Evidence, Candidates and Constraints.

Machine Learning in the 2HDM2S model for Dark Matter Particle Dark Matter: Evidence, Candidates and Constraints

Reference 7

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local_arxiv, observed 2026-05-18T19:21:47.484970Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 8a95e688-b8a6-4439-bd05-95b7dc65975a · outbound

This paper cites Dark Matter Candidates from Particle Physics and Methods of Detection.

Machine Learning in the 2HDM2S model for Dark Matter Dark Matter Candidates from Particle Physics and Methods of Detection

Reference 8

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local_arxiv, observed 2026-05-18T19:21:47.526363Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation dfc896a5-cc07-47ee-a18a-09962f3cd443 · outbound

This paper cites A Natural Two-Higgs-Doublet Model.

Machine Learning in the 2HDM2S model for Dark Matter A Natural Two-Higgs-Doublet Model

Reference 9

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local_arxiv, observed 2026-05-18T19:21:47.520938Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:7bee5e0df7506646679a92f733128203d7914151acd3e07a754cbe3f9b7a73dd

Observation 4733274f-e27b-494b-84d0-3eec69f3de0c · outbound

This paper cites Dark matter annihilation through a lepton-specific Higgs boson.

Machine Learning in the 2HDM2S model for Dark Matter Dark matter annihilation through a lepton-specific Higgs boson

Reference 10

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local_arxiv, observed 2026-05-18T19:21:47.503716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 7033d933-32a7-49b6-a1ff-efa715ec5cb7 · outbound

This paper cites Direct and Indirect Singlet Scalar Dark Matter Detection in the Lepton-Specific two-Higgs-doublet Model.

Machine Learning in the 2HDM2S model for Dark Matter Direct and Indirect Singlet Scalar Dark Matter Detection in the Lepton-Specific two-Higgs-doublet Model

Reference 11

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local_arxiv, observed 2026-05-18T19:21:47.479527Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation fd44fa29-c9f5-4268-a45f-36dabcb3d9d2 · outbound

This paper cites Hints of Standard Model Higgs Boson at the LHC and Light Dark Matter Searches.

Machine Learning in the 2HDM2S model for Dark Matter Hints of Standard Model Higgs Boson at the LHC and Light Dark Matter Searches

Reference 12

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local_arxiv, observed 2026-05-18T19:21:47.497861Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 46b4a188-2719-4dd4-854e-5411c1816cc0 · outbound

This paper cites 2HDM Portal Dark Matter: LHC data and the Fermi-LAT 135 GeV Line.

Machine Learning in the 2HDM2S model for Dark Matter 2HDM Portal Dark Matter: LHC data and the Fermi-LAT 135 GeV Line

Reference 13

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local_arxiv, observed 2026-05-18T19:21:47.509721Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 42df37f9-1bca-41f1-9537-51cf019ee124 · outbound

This paper cites Extending two-Higgs-doublet models by a singlet scalar field - the Case for Dark Matter.

Machine Learning in the 2HDM2S model for Dark Matter Extending two-Higgs-doublet models by a singlet scalar field - the Case for Dark Matter

Reference 14

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local_arxiv, observed 2026-05-18T19:21:47.254565Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 823e426a-ffcb-4350-9eaf-bb38db359beb · outbound

This paper cites Implications of the observation of dark matter self-interactions for singlet scalar dark matter.

Machine Learning in the 2HDM2S model for Dark Matter Implications of the observation of dark matter self-interactions for singlet scalar dark matter

Reference 15

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arxiv_id, observed 2026-05-18T19:21:47.259431Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation a6852d12-6536-44de-906e-a161a27541b5 · outbound

This paper cites The Next-to-Minimal Two Higgs Doublet Model.

Machine Learning in the 2HDM2S model for Dark Matter The Next-to-Minimal Two Higgs Doublet Model

Reference 16

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local_arxiv, observed 2026-05-18T19:21:47.370659Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 396362f9-c344-43e1-8b22-debb87bc2152 · outbound

This paper cites The N2HDM under Theoretical and Experimental Scrutiny.

Machine Learning in the 2HDM2S model for Dark Matter The N2HDM under Theoretical and Experimental Scrutiny

Reference 17

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arxiv_id, observed 2026-05-18T19:21:47.359392Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 2276f10e-c912-44d7-a479-ff0c2c0a0067 · outbound

This paper cites Vacuum Instabilities in the N2HDM.

Machine Learning in the 2HDM2S model for Dark Matter Vacuum Instabilities in the N2HDM

Reference 18

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arxiv_id, observed 2026-05-18T19:21:47.354447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation f1119d42-5121-43d3-bd14-2821b9edd675 · outbound

This paper cites The Dark Phases of the N2HDM.

Machine Learning in the 2HDM2S model for Dark Matter The Dark Phases of the N2HDM

Reference 19

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arxiv_id, observed 2026-05-18T19:21:47.441818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 6aea8e9c-1f82-485b-9795-bb02eef3e9e7 · outbound

This paper cites Electroweak Corrections to Dark Matter Direct Detection in the Dark Singlet Phase of the N2HDM.

Machine Learning in the 2HDM2S model for Dark Matter Electroweak Corrections to Dark Matter Direct Detection in the Dark Singlet Phase of the N2HDM

Reference 20

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arxiv_id, observed 2026-05-18T19:21:47.426328Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 259f3489-1eb0-4b74-89fa-1cc5e62725f2 · outbound

This paper cites Two Higgs Doublets and a Complex Singlet: Disentangling the Decay Topologies and Associated Phenomenology.

Machine Learning in the 2HDM2S model for Dark Matter Two Higgs Doublets and a Complex Singlet: Disentangling the Decay Topologies and Associated Phenomenology

Reference 21

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local_arxiv, observed 2026-05-18T19:21:47.249843Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation f6c6b0d5-b7c0-46de-b9d9-6cde805efa65 · outbound

This paper cites A 96 GeV Higgs Boson in the 2HDM plus Singlet.

Machine Learning in the 2HDM2S model for Dark Matter A 96 GeV Higgs Boson in the 2HDM plus Singlet

Reference 22

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arxiv_id, observed 2026-05-18T19:21:47.338045Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 4872b809-8d7e-4b97-9d3d-c5ed2d704a5c · outbound

This paper cites Phenomenology of the dark matter sector in the 2HDM extended with complex scalar singlet.

Machine Learning in the 2HDM2S model for Dark Matter Phenomenology of the dark matter sector in the 2HDM extended with complex scalar singlet

Reference 23

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arxiv_id, observed 2026-05-18T19:21:47.283460Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:2b4d955a9f43541203f5ec31d56318a6d862bafaee83f40dbdc283762cd2c58a

Observation f0ff0981-7df0-4e73-8199-9052908c887d · outbound

This paper cites Dark Matter Phenomenology in 2HDMS in light of the 95 GeV excess.

Machine Learning in the 2HDM2S model for Dark Matter Dark Matter Phenomenology in 2HDMS in light of the 95 GeV excess

Reference 24

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arxiv_id, observed 2026-05-18T19:21:47.421170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 956f710a-cf3a-4f39-ab3f-6025798520d8 · outbound

This paper cites Vacuum (in)stability in 2HDMS vs N2HDM.

Machine Learning in the 2HDM2S model for Dark Matter Vacuum (in)stability in 2HDMS vs N2HDM

Reference 25

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arxiv_id, observed 2026-05-18T19:21:47.446983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 9cb7a5c0-a091-4a78-8b87-d07ab2e7de37 · outbound

This paper cites Dark Matter in Multi-Singlet Extensions of the Standard Model.

Machine Learning in the 2HDM2S model for Dark Matter Dark Matter in Multi-Singlet Extensions of the Standard Model

Reference 26

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arxiv_id, observed 2026-06-02T02:03:31.831305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:11aef0342c3663aa5dcdef0c4241b7b084ecdf554c33c339ec58afb803e203e4

Observation dee5521a-be7f-43fa-bc89-7cac44272833 · outbound

This paper cites The CP-conserving two-Higgs-doublet model: the approach to the decoupling limit.

Machine Learning in the 2HDM2S model for Dark Matter The CP-conserving two-Higgs-doublet model: the approach to the decoupling limit

Reference 27

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local_arxiv, observed 2026-05-18T19:21:47.468175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:b333dcd1998261861cdbb54af092ff02593edf296d56675bea4ea5cee99d0de2

Observation d2e6b483-1974-4494-bd85-0d179061cbf2 · outbound

This paper cites Boundedness from below in the $U(1)\times U(1)$ three-Higgs-Doublet model.

Machine Learning in the 2HDM2S model for Dark Matter Boundedness from below in the $U(1)\times U(1)$ three-Higgs-Doublet model

Reference 28

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arxiv_id, observed 2026-05-18T19:21:47.431894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:c13ee46b43a22585fe2b109c6fae1ccc514936d8d6e173956fc30162782d8d9e

Observation 312ef783-ca9a-4670-9d94-c328d51f0f9d · outbound

This paper cites Alignment limit in three Higgs-doublet models.

Machine Learning in the 2HDM2S model for Dark Matter Alignment limit in three Higgs-doublet models

Reference 29

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arxiv_id, observed 2026-05-18T19:21:47.343091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation e0c05602-b947-4e4f-86d8-882e318c2d6d · outbound

This paper cites an unresolved cited work.

Machine Learning in the 2HDM2S model for Dark Matter Unresolved cited work

Reference 30

Resolution
unresolved
raw_fallback, observed 2026-05-18T19:22:50.070787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:d8c38a649159485539e806760ab0a760f3559c326d2c91fc7e22f49fb57029c8

Observation 069435f5-16b8-4268-88c5-e760be592486 · outbound

This paper cites Vacuum Stability Conditions From Copositivity Criteria.

Machine Learning in the 2HDM2S model for Dark Matter Vacuum Stability Conditions From Copositivity Criteria

Reference 31

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local_arxiv, observed 2026-05-18T19:21:47.230779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:d361d9210341d10ecc6e7fdded7400391b1913e497ea6d480e7a5c00c5c64b2d

Observation afb4daa1-17e4-4f43-ac37-04aade11437b · outbound

This paper cites BFB conditions on a class of symmetry constrained 3HDM.

Machine Learning in the 2HDM2S model for Dark Matter BFB conditions on a class of symmetry constrained 3HDM

Reference 32

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verified exact
arxiv_id, observed 2026-05-18T19:21:47.240086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:828c219e2b9ee36bd291d8039d0c2ffe73650885aa76914a66d6304807d70048

Observation b8aba86a-a47a-4864-b7c3-9f205ae6b08c · outbound

This paper cites Ping and F.

Machine Learning in the 2HDM2S model for Dark Matter Ping and F

Reference 33

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verified fuzzy
raw_fallback, observed 2026-05-18T19:22:50.073706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:1b8691f1670db77b95ae059062adfb133bad3aeed76750724a3384ce059c903d

Observation 8413759f-fd27-4106-a2f1-846c81c5b07a · outbound

This paper cites James and M.

Machine Learning in the 2HDM2S model for Dark Matter James and M

Reference 34

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raw_fallback, observed 2026-05-18T19:22:50.067583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:eff28c4b08a5c6cb313971bff02a3a1763e5b063506d13f7846b7642aa94466a

Observation 90089d8f-f240-4ae4-83c2-a7d89337c292 · outbound

This paper cites Multi-Higgs doublet models: physical parametrization, sum rules and unitarity bounds.

Machine Learning in the 2HDM2S model for Dark Matter Multi-Higgs doublet models: physical parametrization, sum rules and unitarity bounds

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-05-18T19:21:47.244585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:0f0e47b9372deb04e2b604165c7ec064dd5402c2dc3dfa4ae62e0958e2c2b343

Observation 1e9e9260-ea9f-417d-998b-e930f48c0ca9 · outbound

This paper cites Unitarity bounds for all symmetry-constrained 3HDMs.

Machine Learning in the 2HDM2S model for Dark Matter Unitarity bounds for all symmetry-constrained 3HDMs

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.221000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 14d8f421-dd7a-42e8-bca9-8d1cbb0e30a7 · outbound

This paper cites A precision constraint on multi-Higgs-doublet models.

Machine Learning in the 2HDM2S model for Dark Matter A precision constraint on multi-Higgs-doublet models

Reference 37

Resolution
verified exact
local_arxiv, observed 2026-05-18T19:21:47.225877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:f3a913aed2b273cfcfb99944a2128329a39662dd6781c5384384d6cbb40a6361

Observation 188dedc7-ac53-4ada-a5c6-3e958aac5534 · outbound

This paper cites HiggsTools: BSM scalar phenomenology with new versions of HiggsBounds and HiggsSignals.

Machine Learning in the 2HDM2S model for Dark Matter HiggsTools: BSM scalar phenomenology with new versions of HiggsBounds and HiggsSignals

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.235445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:0c412efe6cf61408c33b0a172368a4d45622f30a922c1782f1aa987d5d0a3e44

Observation 20616549-f355-47ac-9f41-cda1a9efe126 · outbound

This paper cites micrOMEGAs 6.0: N-component dark matter.

Machine Learning in the 2HDM2S model for Dark Matter micrOMEGAs 6.0: N-component dark matter

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.399624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:ecf4fe5ef75483a5e0fee4730763d08b1476875cd2ade03ffb455a0ad0b016e5

Observation dfdb9816-17a8-458b-83f7-0de5893a5cbd · outbound

This paper cites Minimal semi-annihilating $\mathbb{Z}_N$ scalar dark matter.

Machine Learning in the 2HDM2S model for Dark Matter Minimal semi-annihilating $\mathbb{Z}_N$ scalar dark matter

Reference 40

Resolution
verified exact
local_arxiv, observed 2026-05-18T19:21:47.473855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:7ff6e28f6a29cae707955b74000b5e21170382cde5e3f3b0d800e3d2e43705e7

Observation 79f150d1-0ba5-477d-9ea6-7a6c18344548 · outbound

This paper cites Two dark matter candidates: the case of inert doublet and singlet scalars.

Machine Learning in the 2HDM2S model for Dark Matter Two dark matter candidates: the case of inert doublet and singlet scalars

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.296784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:b4a02ca140c04d9e5f028d0924aa92a37e51cb4b086a0ad94fed6855726db481

Observation f8e9cdc0-0fc0-47c7-b512-9f3f6c0cd89b · outbound

This paper cites A Precision Search for WIMPs with Charged Cosmic Rays.

Machine Learning in the 2HDM2S model for Dark Matter A Precision Search for WIMPs with Charged Cosmic Rays

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-05-18T19:21:47.389802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:0679baec977700b3d8dd20a72d5801937b0237f5c251023d24f2c5f9118ac1b0

Observation 8b07af24-664a-4508-9498-88bbf1cea678 · outbound

This paper cites Exploring Parameter Spaces with Artificial Intelligence and Machine Learning Black-Box Optimisation Algorithms.

Machine Learning in the 2HDM2S model for Dark Matter Exploring Parameter Spaces with Artificial Intelligence and Machine Learning Black-Box Optimisation Algorithms

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.302217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:8d45087ffe7a1daf1a769cd710502bc540ba21211b7b6cc11300dee77ff09cc1

Observation 7447101e-0dc2-4d2c-a121-17c5cd543c29 · outbound

This paper cites Combining Evolutionary Strategies and Novelty Detection to go Beyond the Alignment Limit of the $Z_3$ 3HDM.

Machine Learning in the 2HDM2S model for Dark Matter Combining Evolutionary Strategies and Novelty Detection to go Beyond the Alignment Limit of the $Z_3$ 3HDM

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.410173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:79595e567cee7cfda3d2c61b16b4b5cc7d2d5b6331c40d0469da9912abdbbac8

Observation 55677219-6f6a-46b8-8a2a-2ce8f5d2657e · outbound

This paper cites Unearthing large pseudoscalar Yukawa couplings with Machine Learning.

Machine Learning in the 2HDM2S model for Dark Matter Unearthing large pseudoscalar Yukawa couplings with Machine Learning

Reference 45

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.405046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:cb50787a129b5c9c758727b6c5e1c1249ea23e1b4359305b3fa6ecec92dc23e9

Observation d78f9126-b62a-471f-8369-d20979566b63 · outbound

This paper cites Weak Radiative Decays of the B Meson and Bounds on $M_{H^\pm}$ in the Two-Higgs-Doublet Model.

Machine Learning in the 2HDM2S model for Dark Matter Weak Radiative Decays of the B Meson and Bounds on $M_{H^\pm}$ in the Two-Higgs-Doublet Model

Reference 46

Resolution
verified exact
local_arxiv, observed 2026-05-18T19:21:47.451992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:a38392b95cf2d21637921a870dd41aa1d2b20c6bc9850c3d632a15f7ad6f9690

Observation d064de28-8c9b-4b93-a6f6-70427f6209ff · outbound

This paper cites de Souza, N.F.

Machine Learning in the 2HDM2S model for Dark Matter de Souza, N.F

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.326823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:a125e73016a556603f4c766919323744bec11e342ed2e11faa2ef00ea7c08b33

Observation 026b891e-6b5f-43a7-802b-59186f4a16c1 · outbound

This paper cites Constraining the Parameters of High-Dimensional Models with Active Learning.

Machine Learning in the 2HDM2S model for Dark Matter Constraining the Parameters of High-Dimensional Models with Active Learning

Reference 48

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.375886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:0dc0e4cbd71bc13b59c604bfc1ec2e35b029f157756fd6e03a65b11c65957b85

Observation c6766226-8f8d-4e65-9e52-e38d8370dc02 · outbound

This paper cites Efficient sampling of constrained high-dimensional theoretical spaces with machine learning.

Machine Learning in the 2HDM2S model for Dark Matter Efficient sampling of constrained high-dimensional theoretical spaces with machine learning

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.436963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:0e36f2328c1bb1f79d9bc44c183d40fdfc3de2708eb24f907f1050d3592d4679

Observation cdbee315-cab8-4b31-b7fb-14e41b65531f · outbound

This paper cites Active learning BSM parameter spaces.

Machine Learning in the 2HDM2S model for Dark Matter Active learning BSM parameter spaces

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.380349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:0916454599eed092eb801a7ecb5e37b814fc4c070a7a02dbb45a4c0251c7ff06

Observation 65768ebd-a104-4d9a-b3d1-e85aa0a1d299 · outbound

This paper cites Bayesian active search on parameter space: A 95 GeV spin-0 resonance in the (B−L)SSM.

Machine Learning in the 2HDM2S model for Dark Matter Bayesian active search on parameter space: A 95 GeV spin-0 resonance in the (B−L)SSM

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.308530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:e8ea2eab4f8bf72feeeab9a3c4ba9cfa5e63a1d85d11b3257af8c97b2bbfeaf8

Observation f71502b6-c23c-488f-93f9-a51bda525b86 · outbound

This paper cites Constraining the 3HDM Parameter Space using Active Learning.

Machine Learning in the 2HDM2S model for Dark Matter Constraining the 3HDM Parameter Space using Active Learning

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.394545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:f370c1adf16874cd6d52e452e33d46909162018702658a7d30dab1c12632e8af

Observation 3cd01750-1ddd-4a16-9bd7-fdc3345dd675 · outbound

This paper cites Hammad, R.

Machine Learning in the 2HDM2S model for Dark Matter Hammad, R

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.415549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:50c39098ca027ceccc660f04efd5fa933f5cbfef564372db5bbbc27c6c1f8cf5

Observation e1c640f9-8ff1-4662-902e-1f7c14fba828 · outbound

This paper cites A Living Review of Machine Learning for Particle Physics.

Machine Learning in the 2HDM2S model for Dark Matter A Living Review of Machine Learning for Particle Physics

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.321016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:d71903da3e15871ec3c9e49028312db8dd9b3fb43d8c55a7a451d6a447dfdfe9

Observation 23e04b94-eaae-4099-ac84-d464077acc90 · outbound

This paper cites Modern Machine Learning for LHC Physicists.

Machine Learning in the 2HDM2S model for Dark Matter Modern Machine Learning for LHC Physicists

Reference 55

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.314819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:bbf8ce43037b6b079ceac95fdb3b8216da0d96c429a0bd2fb64bf530707dae89

Observation a55715d0-15ad-4db0-ab0c-71ed7f7a49da · outbound

This paper cites Hansen and A.

Machine Learning in the 2HDM2S model for Dark Matter Hansen and A

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T19:22:50.064065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:7e7b5fa8b91a1dee8c2b51ad0db265a2227de58c26c560d5e3f1fa42798f063d

Observation 8dfd2577-07b5-4f42-b190-cadc8bdabe87 · outbound

This paper cites The CMA Evolution Strategy: A Tutorial.

Machine Learning in the 2HDM2S model for Dark Matter The CMA Evolution Strategy: A Tutorial

Reference 57

Resolution
verified exact
local_arxiv, observed 2026-05-18T19:21:47.385099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:5d72665ee727534c2eb4fd01d18124738b226fdbea4b029dbeafc29545fe7579

Observation 5fc90a1d-53e7-4d50-acb7-a89799ad6b26 · outbound

This paper cites Fog on the horizon: a new definition of the neutrino floor for direct dark matter searches.

Machine Learning in the 2HDM2S model for Dark Matter Fog on the horizon: a new definition of the neutrino floor for direct dark matter searches

Reference 58

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.462803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:740799dabe41ba117478ab64bb18dc53383ebd6b9e0c78b845f36fa883266b61

Observation b35b69e7-d0a2-4e4a-84df-e5217a70ef1c · outbound

This paper cites Dark Matter vs. Neutrinos: The effect of astrophysical uncertainties and timing information on the neutrino floor.

Machine Learning in the 2HDM2S model for Dark Matter Dark Matter vs. Neutrinos: The effect of astrophysical uncertainties and timing information on the neutrino floor

Reference 59

Resolution
verified exact
local_arxiv, observed 2026-05-18T19:21:47.457208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:b7113d5c4cf04161a7931717bcc92f6ae699ecfe32768185c5043f8a78bca1aa

Observation e5a8fcf1-11a0-4272-9fed-5c0a08725d1b · outbound

This paper cites Casting a Wide Signal Net with Future Direct Dark Matter Detection Experiments.

Machine Learning in the 2HDM2S model for Dark Matter Casting a Wide Signal Net with Future Direct Dark Matter Detection Experiments

Reference 60

Resolution
verified exact
local_arxiv, observed 2026-05-18T19:21:47.364991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:04a6651de229633d476ed3da3d3f79ce0e89274037af055fa2f74b0afa82ae1f

Observation 3d86753e-32ae-458e-858a-8c89d8350b84 · outbound

This paper cites Beyond the Veil: Charting WIMP Territories at the Neutrino Floor.

Machine Learning in the 2HDM2S model for Dark Matter Beyond the Veil: Charting WIMP Territories at the Neutrino Floor

Reference 61

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.331881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:9336464f31fe10f71ae9e146afff24586efb0957fbaa2e7b8448d0ace9fcc763

Observation 96d8a2ce-52aa-4766-8556-ba44242d5d44 · outbound

This paper cites CYGNUS: Feasibility of a nuclear recoil observatory with directional sensitivity to dark matter and neutrinos.

Machine Learning in the 2HDM2S model for Dark Matter CYGNUS: Feasibility of a nuclear recoil observatory with directional sensitivity to dark matter and neutrinos

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.349029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T19:21:02.625794Z digest=sha256:3ab4334fc1135857dc09d50ef511afd0a8a29aa640b9c85da16dd7a3f8c2427b

Pith citing papers

Observation 2fbfdb84-5529-4033-9a32-cf1631effc8c · inbound

Perturbative unitarity for models with singlet and doublet scalars cites this paper.

Perturbative unitarity for models with singlet and doublet scalars Machine Learning in the 2HDM2S model for Dark Matter

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-04T12:51:49.155762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T12:51:49.155762Z digest=sha256:c67b694409a4dda70a6ff766c4e12f382e3ec86f58ec588e9fc71cd88af71e78

Observation c7d38b12-a9cc-4515-8db8-0c744f465f7c · inbound

BSMArt 2: simpler and faster parameter space scans cites this paper.

BSMArt 2: simpler and faster parameter space scans Machine Learning in the 2HDM2S model for Dark Matter

Reference 93

Resolution
verified exact
local_arxiv, observed 2026-07-02T10:26:52.255378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-28T05:02:44.747731Z digest=sha256:11bfec469da0c5080a678c5a4ad9a75fe6790e1dcfd8f3b727a5142867d0f06e