Paper Citation Record · LEDGER

Deep learning for exploring hadron-hadron interactions

As of 21 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 3 inbound Pith citation observations for arXiv:2501.00374.

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

pith.paper-citation-record.v1
2501.00374 v1

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measured 40 of 40 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-10T22:57:49.859812Z

measured 43 of 43 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:01:08.814028Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T20:21:10.362276Z

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40 of 40 outbound references displayed

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Outbound references

Observation 5dc55f87-b125-4ab6-9e4b-b7c2ed98ca2a · outbound

This paper cites Yukawa, On the Interaction of Elementary Particles I, Proc.

Deep learning for exploring hadron-hadron interactions Yukawa, On the Interaction of Elementary Particles I, Proc

Reference 1

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This paper cites an unresolved cited work.

Deep learning for exploring hadron-hadron interactions Unresolved cited work

Reference 2

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Observation 0d5b83d8-2329-47fb-b2bc-8ba36914d8ce · outbound

This paper cites Kiang, M.

Deep learning for exploring hadron-hadron interactions Kiang, M

Reference 3

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Observation 12dd16e1-4d93-474e-98d3-d5ccc4223035 · outbound

This paper cites Accurate Charge-Dependent Nucleon-Nucleon Potential at Fourth Order of Chiral Perturbation Theory.

Deep learning for exploring hadron-hadron interactions Accurate Charge-Dependent Nucleon-Nucleon Potential at Fourth Order of Chiral Perturbation Theory

Reference 4

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Observation b0e65578-9534-41ad-94ac-456e8c351b96 · outbound

This paper cites Chiral effective field theory and nuclear forces.

Deep learning for exploring hadron-hadron interactions Chiral effective field theory and nuclear forces

Reference 5

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Observation e33de936-6f04-47c8-9c61-64bc0aeb80f3 · outbound

This paper cites Chiral perturbation theory for heavy hadrons and chiral effective field theory for heavy hadronic molecules.

Deep learning for exploring hadron-hadron interactions Chiral perturbation theory for heavy hadrons and chiral effective field theory for heavy hadronic molecules

Reference 6

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Observation 458040d2-8b3b-404c-950c-e2570b46f06c · outbound

This paper cites Baryon-Baryon Interactions and Spin-Flavor Symmetry from Lattice Quantum Chromodynamics.

Deep learning for exploring hadron-hadron interactions Baryon-Baryon Interactions and Spin-Flavor Symmetry from Lattice Quantum Chromodynamics

Reference 7

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Observation b650b984-e5f5-4e1e-95c9-de93c95a371b · outbound

This paper cites Lattice QCD and baryon-baryon interactions: HAL QCD method.

Deep learning for exploring hadron-hadron interactions Lattice QCD and baryon-baryon interactions: HAL QCD method

Reference 8

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Observation 4e4b2119-9446-4158-b639-bb78bac02bc7 · outbound

This paper cites Hadron interactions in lattice QCD.

Deep learning for exploring hadron-hadron interactions Hadron interactions in lattice QCD

Reference 9

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Observation 73a343d4-597d-4d31-a2db-d4edea4c27e0 · outbound

This paper cites Systematics of the HAL QCD Potential at Low Energies in Lattice QCD.

Deep learning for exploring hadron-hadron interactions Systematics of the HAL QCD Potential at Low Energies in Lattice QCD

Reference 10

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Observation b3162c91-ef44-441a-86f2-9583f1692bdd · outbound

This paper cites Hatsuda, Lattice quantum chromodynamics and baryon-baryon inter- actions, Front.

Deep learning for exploring hadron-hadron interactions Hatsuda, Lattice quantum chromodynamics and baryon-baryon inter- actions, Front

Reference 11

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Observation d6639be3-f8f3-47d7-83b7-8f4d4f68bc09 · outbound

This paper cites Pratt, Pion Interferometry for Exploding Sources, Phys.

Deep learning for exploring hadron-hadron interactions Pratt, Pion Interferometry for Exploding Sources, Phys

Reference 12

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Observation f6430926-6218-4f24-881b-e4256a47dc84 · outbound

This paper cites an unresolved cited work.

Deep learning for exploring hadron-hadron interactions Unresolved cited work

Reference 13

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Observation 0db1d3d1-6511-46c9-a2a5-6c4f5106ae55 · outbound

This paper cites Ohnishi, Hadrons, Quark-Gluon Plasma, and Neutron Stars, Springer, 2023, pp.

Deep learning for exploring hadron-hadron interactions Ohnishi, Hadrons, Quark-Gluon Plasma, and Neutron Stars, Springer, 2023, pp

Reference 14

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Observation 2b5116b2-8e18-483e-b843-0b4e33bbf424 · outbound

This paper cites Study of the strong interaction among hadrons with correlations at the LHC.

Deep learning for exploring hadron-hadron interactions Study of the strong interaction among hadrons with correlations at the LHC

Reference 15

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Observation 5ba00da4-a92f-46af-b1ab-7c4b359e7bda · outbound

This paper cites Unveiling the strong interaction among hadrons at the LHC.

Deep learning for exploring hadron-hadron interactions Unveiling the strong interaction among hadrons at the LHC

Reference 16

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Observation 28fb9413-c1aa-4d47-96a7-3a7424924fad · outbound

This paper cites Adam, et al., The Proton- Ω correlation function in Au +Au collisions at √sNN =200 GeV, Phys.

Deep learning for exploring hadron-hadron interactions Adam, et al., The Proton- Ω correlation function in Au +Au collisions at √sNN =200 GeV, Phys

Reference 17

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Observation 41e88e62-22ea-4472-8557-3bc5447b84cb · outbound

This paper cites Three ways to decipher the nature of exotic hadrons: multiplets, three-body hadronic molecules, and correlation functions.

Deep learning for exploring hadron-hadron interactions Three ways to decipher the nature of exotic hadrons: multiplets, three-body hadronic molecules, and correlation functions

Reference 18

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Observation 78cf302f-b55d-4a5b-b492-d560dfcf49cd · outbound

This paper cites Exploring QCD matter in extreme conditions with Machine Learning.

Deep learning for exploring hadron-hadron interactions Exploring QCD matter in extreme conditions with Machine Learning

Reference 19

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Observation 95468d6e-a09f-455b-a68e-704b08501520 · outbound

This paper cites LeCun, Y.

Deep learning for exploring hadron-hadron interactions LeCun, Y

Reference 20

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Observation 04396302-c1ba-48c2-9daf-8059254de974 · outbound

This paper cites Tanaka, A.

Deep learning for exploring hadron-hadron interactions Tanaka, A

Reference 21

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Observation ebab654b-ba4f-4879-bd66-c47a82ad6b2a · outbound

This paper cites Lednicky, V.

Deep learning for exploring hadron-hadron interactions Lednicky, V

Reference 22

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Observation 9c7afe99-be17-4e3f-b691-4bf2af3a7d46 · outbound

This paper cites URL https://web.pa.msu.edu/people/pratts/freecodes/ crab/home.html.

Deep learning for exploring hadron-hadron interactions URL https://web.pa.msu.edu/people/pratts/freecodes/ crab/home.html

Reference 23

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Observation 60dfcd8d-8a58-4d13-9796-74145176c4c8 · outbound

This paper cites A femtoscopic Correlation Analysis Tool using the Schr\"odinger equation (CATS).

Deep learning for exploring hadron-hadron interactions A femtoscopic Correlation Analysis Tool using the Schr\"odinger equation (CATS)

Reference 24

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This paper cites Learning Hadron Emitting Sources with Deep Neural Networks.

Deep learning for exploring hadron-hadron interactions Learning Hadron Emitting Sources with Deep Neural Networks

Reference 25

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Observation e37601e1-d6ac-43c8-938c-79b064061650 · outbound

This paper cites Lattice QCD approach to Nuclear Physics.

Deep learning for exploring hadron-hadron interactions Lattice QCD approach to Nuclear Physics

Reference 26

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Observation ec166314-b062-4bea-845b-30338cae0835 · outbound

This paper cites Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).

Deep learning for exploring hadron-hadron interactions Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Reference 27

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This paper cites Nuclear Force from Lattice QCD.

Deep learning for exploring hadron-hadron interactions Nuclear Force from Lattice QCD

Reference 28

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This paper cites Nuclear Force from Monte Carlo Simulations of Lattice Quantum Chromodynamics.

Deep learning for exploring hadron-hadron interactions Nuclear Force from Monte Carlo Simulations of Lattice Quantum Chromodynamics

Reference 29

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Observation aaac7a06-6c68-414c-bfdc-5125718c5402 · outbound

This paper cites Theoretical Foundation of the Nuclear Force in QCD and its applications to Central and Tensor Forces in Quenched Lattice QCD Simulations.

Deep learning for exploring hadron-hadron interactions Theoretical Foundation of the Nuclear Force in QCD and its applications to Central and Tensor Forces in Quenched Lattice QCD Simulations

Reference 30

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This paper cites Hadron-Hadron Interactions from Imaginary-time Nambu-Bethe-Salpeter Wave Function on the Lattice.

Deep learning for exploring hadron-hadron interactions Hadron-Hadron Interactions from Imaginary-time Nambu-Bethe-Salpeter Wave Function on the Lattice

Reference 31

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This paper cites Lattice QCD and Baryon-Baryon Interactions.

Deep learning for exploring hadron-hadron interactions Lattice QCD and Baryon-Baryon Interactions

Reference 32

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This paper cites Building Hadron Potentials from Lattice QCD with Deep Neural Networks.

Deep learning for exploring hadron-hadron interactions Building Hadron Potentials from Lattice QCD with Deep Neural Networks

Reference 33

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Deep learning for exploring hadron-hadron interactions Unresolved cited work

Reference 34

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Deep learning for exploring hadron-hadron interactions Unresolved cited work

Reference 35

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This paper cites Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help.

Deep learning for exploring hadron-hadron interactions Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help

Reference 36

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This paper cites Most charming dibaryon near unitarity.

Deep learning for exploring hadron-hadron interactions Most charming dibaryon near unitarity

Reference 37

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Observation e103e070-68c7-41d4-9bce-e4e75b49c761 · outbound

This paper cites Dibaryon with highest charm number near unitarity from lattice QCD.

Deep learning for exploring hadron-hadron interactions Dibaryon with highest charm number near unitarity from lattice QCD

Reference 38

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This paper cites Greenacre, P.

Deep learning for exploring hadron-hadron interactions Greenacre, P

Reference 39

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Observation c161d8ef-583d-4035-9d19-6c77707d6658 · outbound

This paper cites Femtoscopy in Relativistic Heavy Ion Collisions: Two Decades of Progress.

Deep learning for exploring hadron-hadron interactions Femtoscopy in Relativistic Heavy Ion Collisions: Two Decades of Progress

Reference 402

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Pith citing papers

Observation 273be7ff-b606-4394-8dc8-213f7849726a · inbound

Towards constraining QCD phase transitions in neutron star interiors: Bayesian Inference with TOV linear response analysis cites this paper.

Towards constraining QCD phase transitions in neutron star interiors: Bayesian Inference with TOV linear response analysis Deep learning for exploring hadron-hadron interactions

Reference 62

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Observation fa9c21a4-8270-4e57-aa06-da1145eafa4f · inbound

Neural network extraction of chromo-electric and chromo-magnetic gluon masses cites this paper.

Neural network extraction of chromo-electric and chromo-magnetic gluon masses Deep learning for exploring hadron-hadron interactions

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T12:14:51.739141Z

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Observation 541a45d6-6563-4a5d-8f6e-779dcaa5b306 · inbound

Four-dimensional QCD equation of state from a quasi-parton model with physics-informed neural networks cites this paper.

Four-dimensional QCD equation of state from a quasi-parton model with physics-informed neural networks Deep learning for exploring hadron-hadron interactions

Reference 47

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