Paper Citation Record · LEDGER

Machine Can Automatically Discover Parametric Functions to Model HEP Data

As of 9 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2607.19750.

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

pith.paper-citation-record.v1
2607.19750 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T11:51:34.166790Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

11 of 11 outbound references displayed

verified exact2
verified fuzzy0
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4068accf-edd0-4c42-9ab7-488109ae322e · outbound

This paper cites Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl.

Machine Can Automatically Discover Parametric Functions to Model HEP Data Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T11:51:33.657577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbb2f9cc-69eb-414e-8f72-98c0bf846e53 · outbound

This paper cites Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at s = 13 TeV.

Machine Can Automatically Discover Parametric Functions to Model HEP Data Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at s = 13 TeV

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T11:51:34.124430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e3adc2d-3c22-4918-84db-b0c5d412ef4e · outbound

This paper cites Search for new resonances in mass distributions of jet pairs using 139 fb ^ -1 of pp collisions at s =13 TeV with the ATLAS detector.

Machine Can Automatically Discover Parametric Functions to Model HEP Data Search for new resonances in mass distributions of jet pairs using 139 fb ^ -1 of pp collisions at s =13 TeV with the ATLAS detector

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T11:51:34.128196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bff759d-fb5c-46c2-a33f-8bf865a2b961 · outbound

This paper cites Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at $\sqrt{s} =$ 13 TeV.

Machine Can Automatically Discover Parametric Functions to Model HEP Data Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at $\sqrt{s} =$ 13 TeV

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T11:51:34.138090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 460dc823-3cd9-42e5-bd6a-f160286c7bce · outbound

This paper cites Search for new resonances in mass distributions of jet pairs using 139 fb$^{-1}$ of $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector.

Machine Can Automatically Discover Parametric Functions to Model HEP Data Search for new resonances in mass distributions of jet pairs using 139 fb$^{-1}$ of $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T11:51:34.142228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09c7c196-d928-4063-b0e9-7fd5c86d27fd · outbound

This paper cites Search for low-mass dijet resonances using trigger-level jets with the ATLAS detector in $pp$ collisions at $\sqrt{s}=13$ TeV.

Machine Can Automatically Discover Parametric Functions to Model HEP Data Search for low-mass dijet resonances using trigger-level jets with the ATLAS detector in $pp$ collisions at $\sqrt{s}=13$ TeV

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T11:51:34.146611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08e46b4a-42f0-465e-aac6-c93adb538310 · outbound

This paper cites SymbolFit: Automatic Parametric Modeling with Symbolic Regression.

Machine Can Automatically Discover Parametric Functions to Model HEP Data SymbolFit: Automatic Parametric Modeling with Symbolic Regression

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T11:51:34.152055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdab7fa9-5f83-479f-8f9b-16e0a19a7645 · outbound

This paper cites Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl.

Machine Can Automatically Discover Parametric Functions to Model HEP Data Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T11:51:34.156297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a860ce6-3ed8-4ccb-82b9-557d669721ad · outbound

This paper cites an unresolved cited work.

Machine Can Automatically Discover Parametric Functions to Model HEP Data Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T11:51:34.160050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c40f6897-6c87-44ce-8958-d951de91b322 · outbound

This paper cites title Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at s = 13 TeV.

Machine Can Automatically Discover Parametric Functions to Model HEP Data title Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at s = 13 TeV

Reference 15

Resolution: verified exact; doi, observed 2026-08-01T11:53:57.789799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b360bb37-f5a2-43dc-92cc-9060ff395e50 · outbound

This paper cites title Search for new resonances in mass distributions of jet pairs using 139 fb ^ -1 of pp collisions at s =13 TeV with the ATLAS detector.

Machine Can Automatically Discover Parametric Functions to Model HEP Data title Search for new resonances in mass distributions of jet pairs using 139 fb ^ -1 of pp collisions at s =13 TeV with the ATLAS detector

Reference 16

Resolution: verified exact; doi, observed 2026-08-01T11:53:57.478293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.