Paper Citation Record · LEDGER

High energy nuclear physics meets Machine Learning

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2303.06752.

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

pith.paper-citation-record.v1
2303.06752 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:51:39.704407Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T19:13:52.713609Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c83a53e8-fe4a-47c5-aac9-9991ce2bce9e · inbound

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

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

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T14:51:39.704407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1fee143-69e8-4285-9c6b-0688750edacc · inbound

Heavy Quarkonium Spectrum and Decay Constants from a Neural-Network-Based Holographic Model cites this paper.

Heavy Quarkonium Spectrum and Decay Constants from a Neural-Network-Based Holographic Model High energy nuclear physics meets Machine Learning

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-03T08:12:16.039795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffea3027-dabb-4ed6-a1e2-43713a6a21a2 · inbound

Probing Proton Structure via Physics-Guided Neural Networks in Holographic QCD cites this paper.

Probing Proton Structure via Physics-Guided Neural Networks in Holographic QCD High energy nuclear physics meets Machine Learning

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:48:08.092909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d61e1da1-741d-4bba-82a5-8a7cdd79b589 · inbound

Study of jet-induced hydro response in high-energy heavy-ion collisions with a flow-matching generative model cites this paper.

Study of jet-induced hydro response in high-energy heavy-ion collisions with a flow-matching generative model High energy nuclear physics meets Machine Learning

Reference 86

Resolution: verified exact; arxiv_id, observed 2026-05-19T22:27:49.338239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 027941e7-387f-4ec4-89ed-0249385320bb · inbound

CNN-Based Online Trigger for QGP Event Selection cites this paper.

CNN-Based Online Trigger for QGP Event Selection High energy nuclear physics meets Machine Learning

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:13:52.714993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ba2b27ad-4436-4446-adc6-f9cdda231e5b · inbound

Neural-network excited states of $A=4$ nuclei and hypernuclei cites this paper.

Neural-network excited states of $A=4$ nuclei and hypernuclei High energy nuclear physics meets Machine Learning

Reference 18

Resolution: malformed identifier; arxiv_id, observed 2026-06-28T20:42:37.149028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f17895eb-e9a7-43a0-b99c-45fe943295bf · inbound

NNStar: An end-to-end AI agent for nuclear matter and neutron star physics cites this paper.

NNStar: An end-to-end AI agent for nuclear matter and neutron star physics High energy nuclear physics meets Machine Learning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-02T03:23:37.455249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.