Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM | Paper Citation Record

Observation 2e14639b-7122-4e3d-bcd7-94432dc76c3f · outbound

This paper cites 1331 Project.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM 1331 Project

Reference 1

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Observation af48e357-b2ac-4333-b1ad-ad3a564bf516 · outbound

This paper cites Determination of the Equation of State of Dense Matter.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Determination of the Equation of State of Dense Matter

Reference 2

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Observation d7d9c219-135c-4110-929d-a7ebec49c926 · outbound

This paper cites The phase diagram of dense QCD.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM The phase diagram of dense QCD

Reference 3

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Observation 8cfc17a1-a327-4f28-8524-b57741f18e13 · outbound

This paper cites Dense Nuclear Matter Equation of State from Heavy-Ion Collisions.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Dense Nuclear Matter Equation of State from Heavy-Ion Collisions

Reference 4

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Observation b6d6a171-b0c6-410b-b591-5f3c070c1368 · outbound

This paper cites Guoet al., Eur.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Guoet al., Eur

Reference 5

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Observation 8341bb38-d88c-400d-b88c-70d718268273 · outbound

This paper cites Klochkov, C.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Klochkov, C

Reference 6

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Observation 1df13816-6bf4-48ca-aeea-4266402787c4 · outbound

This paper cites Bulk Properties of the Medium Produced in Relativistic Heavy-Ion Collisions from the Beam Energy Scan Program.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Bulk Properties of the Medium Produced in Relativistic Heavy-Ion Collisions from the Beam Energy Scan Program

Reference 7

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Observation e5f3afd9-a567-44fe-ab81-539798158a91 · outbound

This paper cites Abgaryanet al.[the MPD Collaboration], Eur.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Abgaryanet al.[the MPD Collaboration], Eur

Reference 8

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Observation 613d1964-c0fd-42a7-b9f1-f834f8ee7e13 · outbound

This paper cites Syresinet al., Chin.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Syresinet al., Chin

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:36:37.511488Z

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Observation 2aefd10e-9fea-463e-a0d8-29a9bef6087d · outbound

This paper cites Anisotropic flow, flow fluctuation and flow decorrelation in relativistic heavy-ion collisions: the roles of sub-nucleon structure and shear viscosity.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Anisotropic flow, flow fluctuation and flow decorrelation in relativistic heavy-ion collisions: the roles of sub-nucleon structure and shear viscosity

Reference 10

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Observation 981ab2c2-618d-4f47-a3c8-a02eb972e94e · outbound

This paper cites Wolter et al.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Wolter et al

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:36:37.199699Z

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Observation bfde7207-85f2-46a6-a24d-d656238170f0 · outbound

This paper cites Energy Dependence of Moments of Net-proton Multiplicity Distributions at RHIC.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Energy Dependence of Moments of Net-proton Multiplicity Distributions at RHIC

Reference 12

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Observation 724c58da-6d39-4e19-935e-4d6d21bc852d · outbound

This paper cites Zhang, Y.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Zhang, Y

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:36:37.124067Z

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Observation c2719cda-2903-44a6-a7ad-5a75b8e12845 · outbound

This paper cites Cavata, M.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Cavata, M

Reference 14

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Observation 9ea3da2c-1260-4097-ab24-b93384cebe0f · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 15

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Observation e144e047-6cf0-408c-8150-27c3d6c4678c · outbound

This paper cites Centrality determination of Au+Au collisions at 1.23A GeV with HADES.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Centrality determination of Au+Au collisions at 1.23A GeV with HADES

Reference 16

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Observation a8da3ad5-0d9e-4ea7-bcaf-71162b11211c · outbound

This paper cites Identified particle production, azimuthal anisotropy, and interferometry measurements in Au+Au collisions at $\sqrt{s_{NN}}$ = 9.2 GeV.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Identified particle production, azimuthal anisotropy, and interferometry measurements in Au+Au collisions at $\sqrt{s_{NN}}$ = 9.2 GeV

Reference 17

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Observation 25047a64-6279-4148-a28a-e8a14c58eae5 · outbound

This paper cites Inclusive charged hadron elliptic flow in Au + Au collisions at $\sqrt{s_{NN}}$ = 7.7 - 39 GeV.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Inclusive charged hadron elliptic flow in Au + Au collisions at $\sqrt{s_{NN}}$ = 7.7 - 39 GeV

Reference 18

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Observation 95adc77c-ecdb-4ec8-a416-314697ce204a · outbound

This paper cites Adamet al.(STAR), Phys.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Adamet al.(STAR), Phys

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:36:37.135861Z

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Observation 2d187d78-d4fb-4a1b-a6ed-3056ab2905fe · outbound

This paper cites Adam et al.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Adam et al

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:36:37.152627Z

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Observation a2440828-5c5c-4bfb-a4ac-259b34bcad57 · outbound

This paper cites Abdallah et al.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Abdallah et al

Reference 21

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Observation f166afdf-6894-441e-a7ba-3ab4ebc5f819 · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:36:37.088684Z

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Observation af1acf90-c835-49ff-a9c6-567f83d160a8 · outbound

This paper cites Lukasiket al., Phys.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Lukasiket al., Phys

Reference 23

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Observation adda7174-c718-4265-a5e0-3bdc2339b984 · outbound

This paper cites Directed and elliptic flow in Au + Au at intermediate energies.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Directed and elliptic flow in Au + Au at intermediate energies

Reference 24

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Observation 2b0ac8ef-b605-458f-9eca-780433356a3b · outbound

This paper cites Universality of Spectator Fragmentation at Relativistic Bombarding Energies.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Universality of Spectator Fragmentation at Relativistic Bombarding Energies

Reference 25

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Observation a9cf20e1-7de6-4c08-9db7-c4482ac3d4cc · outbound

This paper cites Central Collisions of Au on Au at 150, 250 and 400 A MeV.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Central Collisions of Au on Au at 150, 250 and 400 A MeV

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.101381Z

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Observation 277dc201-1601-4730-a9f4-68c6e66fcee8 · outbound

This paper cites Constraining the nuclear matter equation of state around twice saturation density.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Constraining the nuclear matter equation of state around twice saturation density

Reference 27

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Observation 958609ef-ad5e-406c-a9bf-1376e60e8e72 · outbound

This paper cites Search for the QCD Critical Point with Fluctuations of Conserved Quantities in Relativistic Heavy-Ion Collisions at RHIC : An Overview.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Search for the QCD Critical Point with Fluctuations of Conserved Quantities in Relativistic Heavy-Ion Collisions at RHIC : An Overview

Reference 28

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This paper cites Charged pion production in au+au collision at√sN N = 3.2-4.5 gev with the star detector,.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Charged pion production in au+au collision at√sN N = 3.2-4.5 gev with the star detector,

Reference 29

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This paper cites Heavy ion collisions from $\sqrt{s_{NN}}$ of 62.4 GeV down to 4 GeV in the EPOS4 framework.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Heavy ion collisions from $\sqrt{s_{NN}}$ of 62.4 GeV down to 4 GeV in the EPOS4 framework

Reference 30

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Observation 19aea065-e338-4056-93f9-42b63fd97995 · outbound

This paper cites Qu, J.-Y.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Qu, J.-Y

Reference 31

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This paper cites Physically Interpretable Machine Learning for nuclear masses.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Physically Interpretable Machine Learning for nuclear masses

Reference 32

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Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 33

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Observation c4230fff-775d-4bca-8ca9-16ce6f00dfa0 · outbound

This paper cites Nuclear mass predictions using machine learning models.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Nuclear mass predictions using machine learning models

Reference 34

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Observation 86c568b2-9124-4ee8-b8d6-2bea1ce96a3d · outbound

This paper cites Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning

Reference 35

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This paper cites Machine learning transforms the inference of the nuclear equation of state.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Machine learning transforms the inference of the nuclear equation of state

Reference 36

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Observation 85696c58-a4ea-4894-9c78-69b3cb7c1ac4 · outbound

This paper cites Inferring the Equation of State from Neutron Star Observables via Machine Learning.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Inferring the Equation of State from Neutron Star Observables via Machine Learning

Reference 37

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Observation aea60750-72ad-484e-97ff-a0a99a7c2b73 · outbound

This paper cites Bayesian inference of nuclear incompressibility from collective flow in mid-central Au+Au collisions at 400--1500 MeV/nucleon.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Bayesian inference of nuclear incompressibility from collective flow in mid-central Au+Au collisions at 400--1500 MeV/nucleon

Reference 38

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Observation c3149169-0d3d-4c23-8e6a-8c7a87b667bf · outbound

This paper cites Solving Schrodinger equations using physically constrained neural network.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Solving Schrodinger equations using physically constrained neural network

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.144053Z

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Observation bdb7f318-231e-4cf9-a831-f5157bb089a7 · outbound

This paper cites Constraining the Woods-Saxon potential in fusion reactions based on the neural network.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Constraining the Woods-Saxon potential in fusion reactions based on the neural network

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.232378Z

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Observation ec8417dc-d6f6-4ad1-b019-f97e56bff1c4 · outbound

This paper cites Energy-Embedded Neural Solvers for One-Dimensional Quantum Systems.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Energy-Embedded Neural Solvers for One-Dimensional Quantum Systems

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.126858Z

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Observation e7e685d8-dd9e-4580-a4e7-a6e2265c866f · outbound

This paper cites DONG and L.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM DONG and L

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:36:37.519665Z

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Observation 6a5c043c-3704-471e-b1ad-219cae8479ff · outbound

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Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:36:37.551320Z

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Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:36:37.552970Z

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Observation f993232e-0167-4a3f-b813-f91b49da61ce · outbound

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Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:36:37.557698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f757b5fa-be03-42ca-baee-8a122e9298da · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:36:37.549695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d9d2a898-f331-4ffe-88da-8cf47f3a17ff · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:36:37.540724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8314c4bd-bacd-4eec-8776-17b2958e1956 · outbound

This paper cites Huang, K.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Huang, K

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:36:37.228145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d3f7fc9b-b557-45b8-9d9a-b9573b556bd6 · outbound

This paper cites High Precision Binding Energies from Physics Informed Machine Learning.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM High Precision Binding Energies from Physics Informed Machine Learning

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.216076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 31953ea5-fc52-4d48-a0a5-89ef6ae21de3 · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:36:37.548001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e13f42bc-4b25-48fe-9004-c97555f0e080 · outbound

This paper cites Jyothish, G.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Jyothish, G

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:36:37.546219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4ff1273c-23d5-4330-bf31-1bb72c54ff14 · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:36:37.213300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9b953ebd-b83f-41bd-a10c-10f08ae6fac3 · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:36:37.539037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aaff9c4a-2f49-4826-bd6d-5c8ed0817e5d · outbound

This paper cites Nuclear charge radius predictions by kernel ridge regression with odd-even effects.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Nuclear charge radius predictions by kernel ridge regression with odd-even effects

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.196181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 653028b9-bf18-48b9-867a-0bb5baf5a5cb · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:36:37.559360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a73f9f41-cb00-4266-9b48-1b9907bbd54f · outbound

This paper cites Predictions of nuclear $\beta$-decay half-lives with machine learning and their impacts on $r$ process.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Predictions of nuclear $\beta$-decay half-lives with machine learning and their impacts on $r$ process

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.118087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 47ea01f6-3c72-464a-98ea-638e0f2eccd2 · outbound

This paper cites Importance of physical information on the prediction of heavy-ion fusion cross section with machine learning.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Importance of physical information on the prediction of heavy-ion fusion cross section with machine learning

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.191267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 455ecba7-ec65-453e-97aa-35fdded9b48a · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:36:37.070758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8f32faa7-d30e-4173-b1db-71332519f283 · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:36:37.521607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 94728934-420e-4871-8fca-e01dbe4a7606 · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:36:37.123614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3457dd0a-f79e-4787-a815-ed1d46ee186c · outbound

This paper cites De Sanctis, M.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM De Sanctis, M

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:36:37.533494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 918eb675-6e53-4cc0-9e7c-fa0253070f0d · outbound

This paper cites David, M.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM David, M

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:36:37.537524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f380a99f-647c-45be-9de4-54a5c4f7f80e · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:36:37.539293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ea1a6e45-24bc-4263-897f-2ee9d6bdd40c · outbound

This paper cites Galaktionov, V.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Galaktionov, V

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:36:37.546016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 347ba203-7760-49f2-b65b-00c266bb984c · outbound

This paper cites Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural network.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural network

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.063514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3d72951c-0f07-4ddd-920f-555e7078bc43 · outbound

This paper cites Determination of impact parameter in high-energy heavy-ion collisions via deep learning.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Determination of impact parameter in high-energy heavy-ion collisions via deep learning

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.235370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 33b4fd19-2e6d-4b3f-a9dd-f6edda1a4919 · outbound

This paper cites Estimation of Impact Parameter and Transverse Spherocity in heavy-ion collisions at the LHC energies using Machine Learning.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Estimation of Impact Parameter and Transverse Spherocity in heavy-ion collisions at the LHC energies using Machine Learning

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.223821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f1901927-d8f5-4402-8eb2-7c75fdb22946 · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:36:37.554614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b2889a06-a7e9-49d3-9c26-3739d3cfbe3e · outbound

This paper cites Application of machine learning in the determination of impact parameter in the $^{132}$Sn+$^{124}$Sn system.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Application of machine learning in the determination of impact parameter in the $^{132}$Sn+$^{124}$Sn system

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.221227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d052c3e7-ae89-4512-9ecf-27916657400c · outbound

This paper cites Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.161312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c989d288-3bbf-4699-a384-4543d9929c9e · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:36:37.535720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 68c46701-f032-460d-afa0-a6a48b929e8a · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:36:37.529540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0c510a7c-7a7b-4731-a56c-2cc94a0ed73b · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:36:37.529816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ea262a40-6690-43f0-8460-8dbce76957b7 · outbound

This paper cites A Multi-Phase Transport Model for Relativistic Heavy Ion Collisions.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM A Multi-Phase Transport Model for Relativistic Heavy Ion Collisions

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.157698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5f02d819-4165-4eb3-add8-3d7a3f929f29 · outbound

This paper cites Study of relativistic nuclear collisions at AGS energies from p+Be to Au+Au with hadronic cascade model.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Study of relativistic nuclear collisions at AGS energies from p+Be to Au+Au with hadronic cascade model

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.203726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3ad29e06-72d3-4818-9284-07805150164c · outbound

This paper cites Mean-Field Effects on Collective Flows in High-Energy Heavy-Ion Collisions at 2-158A GeV energies.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Mean-Field Effects on Collective Flows in High-Energy Heavy-Ion Collisions at 2-158A GeV energies

Reference 76

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.152485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 85561e42-413d-4b87-b480-4b8214186a99 · outbound

This paper cites Mean-field update in the JAM microscopic model: Mean-field effects on collective flow in high-energy heavy-ion collisions at $\sqrt{s_{NN}}=2-20$ GeV energies.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Mean-field update in the JAM microscopic model: Mean-field effects on collective flow in high-energy heavy-ion collisions at $\sqrt{s_{NN}}=2-20$ GeV energies

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.172696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3d20d74d-7fe0-4730-90e3-57dc965d4a44 · outbound

This paper cites Effects of Initial Density Fluctuations on Cumulants in Au + Au Collisions at $\sqrt{s_{NN}}$ = 7.7 GeV.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Effects of Initial Density Fluctuations on Cumulants in Au + Au Collisions at $\sqrt{s_{NN}}$ = 7.7 GeV

Reference 78

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.095012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2d98cad1-175d-49e9-bb8b-29149de3dd73 · outbound

This paper cites Elliptic flow splitting as a probe of the QCD phase structure at finite baryon chemical potential.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Elliptic flow splitting as a probe of the QCD phase structure at finite baryon chemical potential

Reference 79

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.149870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5c16993a-1e88-476f-a8fb-ebe37480d16e · outbound

This paper cites Equation of state dependence of directed flow in a microscopic transport model.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Equation of state dependence of directed flow in a microscopic transport model

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.244294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c9357720-78c7-483f-97da-2dbb2804df6a · outbound

This paper cites Effects of in-medium nucleon-nucleon cross section on collective flow and nuclear stopping in heavy-ion collisions in the Fermi-energy domain.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Effects of in-medium nucleon-nucleon cross section on collective flow and nuclear stopping in heavy-ion collisions in the Fermi-energy domain

Reference 81

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.135904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5f6fbf09-b40b-4117-b18a-c7e9899cd298 · outbound

This paper cites an unresolved cited work.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Unresolved cited work

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:36:37.180968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 076c504d-8b57-4214-9034-8bc4dbe5ac67 · outbound

This paper cites Liu, J.-P.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Liu, J.-P

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:36:37.531494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ccd82ace-1e11-4637-b7b2-08b260611f0f · outbound

This paper cites Insights on pion production mechanism and symmetry energy at high density.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Insights on pion production mechanism and symmetry energy at high density

Reference 84

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.246953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9e550034-18f9-4a6b-b089-7f46daa2d0e1 · outbound

This paper cites Decoding the nuclear symmetry energy event-by-event in heavy-ion collisions with machine learning.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Decoding the nuclear symmetry energy event-by-event in heavy-ion collisions with machine learning

Reference 85

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.214479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 11f1c36a-1475-429b-bd6b-1e00552d4d4d · outbound

This paper cites A fast centrality-meter for heavy-ion collisions at the CBM experiment.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM A fast centrality-meter for heavy-ion collisions at the CBM experiment

Reference 86

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.175420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6ee0ee50-91d4-4be3-b4cd-39875e44592a · outbound

This paper cites Cluster Scanning: a novel approach to resonance searches.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Cluster Scanning: a novel approach to resonance searches

Reference 87

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.242524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c34cc7d4-333e-4cc4-9e1c-102589fe224a · outbound

This paper cites Cluster Structures with Machine Learning Support in Neutron Star M-R relations.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Cluster Structures with Machine Learning Support in Neutron Star M-R relations

Reference 88

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:36:37.208095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 11a566fe-9591-4546-813d-504cad6a810b · outbound

This paper cites Zhang, S.

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM Zhang, S

Reference 89

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:36:37.217971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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