Paper Citation Record · LEDGER

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD

As of 14 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 1 inbound Pith citation observation for arXiv:2411.14902.

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

pith.paper-citation-record.v1
2411.14902 v3

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

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measured 69 of 69 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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External citation measurements

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Outbound references

Observation 2219c5af-b3bc-4563-bcdb-9b564618aa09 · outbound

This paper cites Greensite, An Introduction to the Confinement Problem, Lecture Notes in Physics (Springer Berlin Heidelberg, 2011).

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Greensite, An Introduction to the Confinement Problem, Lecture Notes in Physics (Springer Berlin Heidelberg, 2011)

Reference 1

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This paper cites Introduction to Lattice QCD.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Introduction to Lattice QCD

Reference 2

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This paper cites Di Giacomo, M.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Di Giacomo, M

Reference 3

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This paper cites Fukugita and T.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Fukugita and T

Reference 4

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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Unresolved cited work

Reference 5

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Observation ddd461bd-f66d-4b09-9e57-925c3daaca9d · outbound

This paper cites Cea and L.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Cea and L

Reference 6

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Observation f54894c4-7759-46a5-ac87-3a3d8632135e · outbound

This paper cites Dual superconductivity in the SU(2) pure gauge vacuum: a lattice study.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Dual superconductivity in the SU(2) pure gauge vacuum: a lattice study

Reference 7

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This paper cites Observing Long Colour Flux Tubes in SU(2) Lattice Gauge Theory.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Observing Long Colour Flux Tubes in SU(2) Lattice Gauge Theory

Reference 8

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This paper cites Luscher, G.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Luscher, G

Reference 9

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Observation 5600812b-7d46-4820-973b-9576403675de · outbound

This paper cites Baker, J.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Baker, J

Reference 10

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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Unresolved cited work

Reference 11

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This paper cites Chromoelectric flux tubes and coherence length in QCD.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Chromoelectric flux tubes and coherence length in QCD

Reference 12

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This paper cites The confining color field in SU(3) gauge theory.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD The confining color field in SU(3) gauge theory

Reference 13

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This paper cites Galsandorj, S.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Galsandorj, S

Reference 14

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Observation f6c000ab-f690-4e78-8dfa-86a82b7c8d79 · outbound

This paper cites Unveiling the flux tube structure in full QCD.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Unveiling the flux tube structure in full QCD

Reference 15

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This paper cites Investigating the flux tube structure within full QCD.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Investigating the flux tube structure within full QCD

Reference 16

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This paper cites String Breaking in Non-Abelian Gauge Theories with Fundamental Matter Fields.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD String Breaking in Non-Abelian Gauge Theories with Fundamental Matter Fields

Reference 17

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Observation 00154ced-da0e-4877-acc9-f7ec6f4326cb · outbound

This paper cites String breaking with Wilson loops?.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD String breaking with Wilson loops?

Reference 18

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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Unresolved cited work

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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Unresolved cited work

Reference 20

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Observation 6bd400b4-c9f2-43d0-b30f-abe864faab5e · outbound

This paper cites Partial restoration of chiral symmetry in a confining string.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Partial restoration of chiral symmetry in a confining string

Reference 21

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Observation dbbfa263-85fd-4ac3-bcc0-2f356d10dbe1 · outbound

This paper cites Partial restoration of chiral symmetry in the color flux tube.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Partial restoration of chiral symmetry in the color flux tube

Reference 22

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This paper cites Exploring quantum entanglement in chiral symmetry partial restoration with 1+1 string model.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Exploring quantum entanglement in chiral symmetry partial restoration with 1+1 string model

Reference 23

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This paper cites Locating Quark-Antiquark String Breaking in QCD through Chiral Symmetry Restoration and Hawking-Unruh Effect.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Locating Quark-Antiquark String Breaking in QCD through Chiral Symmetry Restoration and Hawking-Unruh Effect

Reference 24

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This paper cites Forte, L.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Forte, L

Reference 25

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This paper cites Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning

Reference 26

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This paper cites Deep Learning and its Application to LHC Physics.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Deep Learning and its Application to LHC Physics

Reference 27

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This paper cites Radovic, M.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Radovic, M

Reference 28

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This paper cites Machine Learning in High Energy Physics Community White Paper.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Machine Learning in High Energy Physics Community White Paper

Reference 29

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This paper cites Machine learning and the physical sciences.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Machine learning and the physical sciences

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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Machine and Deep Learning Applications in Particle Physics

Reference 31

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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Modern Machine Learning and Particle Physics

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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Machine Learning in the Search for New Fundamental Physics

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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Machine Learning in Nuclear Physics

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This paper cites Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

Reference 35

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This paper cites Deep learning assisted jet tomography for the study of Mach cones in QGP.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Deep learning assisted jet tomography for the study of Mach cones in QGP

Reference 36

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This paper cites Deep-learning quasi-particle masses from QCD equation of state.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Deep-learning quasi-particle masses from QCD equation of state

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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD High energy nuclear physics meets Machine Learning

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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Exploring QCD matter in extreme conditions with Machine Learning

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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Unresolved cited work

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This paper cites Pang, Studying high-energy nuclear physics with machine learning, Int.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Pang, Studying high-energy nuclear physics with machine learning, Int

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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Phase Transition Study meets Machine Learning

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This paper cites Neural Network Modeling of Heavy-Quark Potential from Holography.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Neural Network Modeling of Heavy-Quark Potential from Holography

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This paper cites Exploring Transport Properties of Quark-Gluon Plasma in Flavor-Dependent Systems with a Holographic Model.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Exploring Transport Properties of Quark-Gluon Plasma in Flavor-Dependent Systems with a Holographic Model

Reference 44

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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Omana Kuttan, J

Reference 45

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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Unresolved cited work

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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Unresolved cited work

Reference 47

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This paper cites Heavy quark potential in quark-gluon Plasma: Deep neural network meets lattice quantum chromodynamics.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Heavy quark potential in quark-gluon Plasma: Deep neural network meets lattice quantum chromodynamics

Reference 48

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This paper cites Holographic complex potential of a quarkonium from deep learning.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Holographic complex potential of a quarkonium from deep learning

Reference 49

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This paper cites Flavor dependent Critical endpoint from holographic QCD through machine learning.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Flavor dependent Critical endpoint from holographic QCD through machine learning

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This paper cites Machine learning holographic black hole from lattice QCD equation of state.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Machine learning holographic black hole from lattice QCD equation of state

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This paper cites Analysis of strong coupling constant with machine learning and its application.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Analysis of strong coupling constant with machine learning and its application

Reference 52

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This paper cites KAN: Kolmogorov-Arnold Networks.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD KAN: Kolmogorov-Arnold Networks

Reference 53

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Observation b7e51974-485d-44b0-8246-2d719d95a3a7 · outbound

This paper cites KAN 2.0: Kolmogorov-Arnold Networks Meet Science.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD KAN 2.0: Kolmogorov-Arnold Networks Meet Science

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T14:51:39.781114Z

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Observation 9248a6a7-0263-4bf9-8b28-dbef6d32caa5 · outbound

This paper cites Structure of Abrikosov Vortices in SU(2) Lattice Gauge Theory.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Structure of Abrikosov Vortices in SU(2) Lattice Gauge Theory

Reference 55

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This paper cites Schilling, G.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Schilling, G

Reference 56

Resolution: verified exact; raw_fallback, observed 2026-08-12T14:51:40.051155Z

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This paper cites Towards Abelian-like formulation of the dual gluodynamics.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Towards Abelian-like formulation of the dual gluodynamics

Reference 57

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Observation 3c3f37f3-28a2-49ab-9e98-33fdd3ee781c · outbound

This paper cites Vacuum type of SU(2) gluodynamics in maximally Abelian and Landau gauges.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Vacuum type of SU(2) gluodynamics in maximally Abelian and Landau gauges

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:51:39.947873Z

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This paper cites Gauge invariance of color confinement due to the dual Meissner effect caused by Abelian monopoles.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Gauge invariance of color confinement due to the dual Meissner effect caused by Abelian monopoles

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:51:39.925176Z

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Observation 69edf8f6-a12a-40d2-8688-f3cbe18eca43 · outbound

This paper cites an unresolved cited work.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Unresolved cited work

Reference 60

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Observation e16e278d-7b85-454e-9c01-35216a5d5561 · outbound

This paper cites an unresolved cited work.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:51:41.084268Z

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Observation fc5de9d4-c8b6-4f09-a62a-dcc373fb1692 · outbound

This paper cites Witten, Superconducting Strings, Nucl.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Witten, Superconducting Strings, Nucl

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T14:51:39.818882Z

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Observation 23d45682-2cd1-4a25-99d0-0f27d6c9db98 · outbound

This paper cites Hornik, M.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Hornik, M

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T14:51:39.823320Z

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Observation b5982d95-e307-4fa8-969d-d8d9d0785cae · outbound

This paper cites Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of control, signals and systems 2, 303 (1989).

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of control, signals and systems 2, 303 (1989)

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:51:41.047709Z

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Observation 9f9585e3-0015-4489-9034-8401795f1947 · outbound

This paper cites Haykin, Neural networks: a comprehensive foundation (Prentice Hall PTR, 1998).

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Haykin, Neural networks: a comprehensive foundation (Prentice Hall PTR, 1998)

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:51:41.031864Z

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Observation d03fb70a-e163-4cd9-a148-3825ec90a3d4 · outbound

This paper cites O’Malley, E.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD O’Malley, E

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:51:41.016735Z

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Observation 8585b329-8291-42b0-bd82-edb379db4075 · outbound

This paper cites Benchmarks for a Global Extraction of Information from Deeply Virtual Exclusive Scattering.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD Benchmarks for a Global Extraction of Information from Deeply Virtual Exclusive Scattering

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T14:51:39.841887Z

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Observation 7155c29f-1a30-4d5b-bd77-c3e555bf12f6 · outbound

This paper cites VAIM-CFF: A variational autoencoder inverse mapper solution to Compton form factor extraction from deeply virtual exclusive reactions.

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD VAIM-CFF: A variational autoencoder inverse mapper solution to Compton form factor extraction from deeply virtual exclusive reactions

Reference 68

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Pith citing papers

Observation fdf48d29-2427-4b05-a23b-747b364ab2a5 · inbound

Extraction of the color dipole amplitude with physics-informed neural networks cites this paper.

Extraction of the color dipole amplitude with physics-informed neural networks Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:37:53.147207Z

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