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Paper Citation Record · LEDGER

Machine Learning in High Energy Physics Community White Paper

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:1807.02876.

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

pith.paper-citation-record.v1
1807.02876 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:44:15.466332Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T06:37:42.831820Z

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0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c82daed4-a8c0-4b80-b5fc-b369ad7672e0 · inbound

Improving robustness of jet tagging algorithms with adversarial training: exploring the loss surface cites this paper.

Improving robustness of jet tagging algorithms with adversarial training: exploring the loss surface Machine Learning in High Energy Physics Community White Paper

Reference 1

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local_arxiv, observed 2026-05-24T09:49:18.017511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation c51d6248-ca94-4da8-897a-4c350b115840 · inbound

A Step Toward Interpretability: Smearing the Likelihood cites this paper.

A Step Toward Interpretability: Smearing the Likelihood Machine Learning in High Energy Physics Community White Paper

Reference 4

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Observation c926feef-15cd-4ed6-853e-d824c8dacd82 · inbound

Multi-scale Optimal Transport for Complete Collider Events cites this paper.

Multi-scale Optimal Transport for Complete Collider Events Machine Learning in High Energy Physics Community White Paper

Reference 3

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Observation 568f866a-1fa8-414b-9708-ecbe1c38a45c · inbound

Models for differential cross section in proton-proton scattering and their implications at ISR and LHC energies cites this paper.

Models for differential cross section in proton-proton scattering and their implications at ISR and LHC energies Machine Learning in High Energy Physics Community White Paper

Reference 60

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Observation fcb9207a-910d-42b1-b789-f1c1dd4e21e0 · inbound

Vector Boson Fusion Signatures of Superheavy Majorana Neutrinos at Muon Colliders cites this paper.

Vector Boson Fusion Signatures of Superheavy Majorana Neutrinos at Muon Colliders Machine Learning in High Energy Physics Community White Paper

Reference 82

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Observation c11cc1a6-df1f-41da-8be3-571f86283655 · inbound

Shedding Light on Dark Matter at the LHC with Machine Learning cites this paper.

Shedding Light on Dark Matter at the LHC with Machine Learning Machine Learning in High Energy Physics Community White Paper

Reference 17

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Observation 35892793-7f99-4c19-8140-ed18006d731d · inbound

KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging cites this paper.

KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging Machine Learning in High Energy Physics Community White Paper

Reference 4

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verified exact
local_arxiv, observed 2026-05-17T01:08:47.480090Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 2bb20d63-9981-4ab0-b56d-d8dd5ac82880 · inbound

KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging cites this paper.

KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging Machine Learning in High Energy Physics Community White Paper

Reference 4

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Observation f151feb1-5379-4e21-98f5-a5803a494ee4 · inbound

HEPTAPOD: Orchestrating High Energy Physics Workflows Towards Autonomous Agency cites this paper.

HEPTAPOD: Orchestrating High Energy Physics Workflows Towards Autonomous Agency Machine Learning in High Energy Physics Community White Paper

Reference 32

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Observation bc601e44-daa6-44c7-b9f9-21f55adf7b00 · inbound

Enhanced sensitivity to the $H \to Z\gamma \to \ell^+\ell^-\gamma$ decay at the LHC using machine learning and novel kinematic observables cites this paper.

Enhanced sensitivity to the $H \to Z\gamma \to \ell^+\ell^-\gamma$ decay at the LHC using machine learning and novel kinematic observables Machine Learning in High Energy Physics Community White Paper

Reference 26

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verified exact
local_arxiv, observed 2026-05-16T13:07:54.626356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 66172079-37c4-44d6-96aa-912f54146cdc · 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 Machine Learning in High Energy Physics Community White Paper

Reference 27

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arxiv_id, observed 2026-05-13T18:48:08.228094Z

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Observation 414c1848-30ae-4226-8b59-4213e14c0b82 · inbound

RooAgent: An LLM Agent for Root-Based High Energy Physics Analysis cites this paper.

RooAgent: An LLM Agent for Root-Based High Energy Physics Analysis Machine Learning in High Energy Physics Community White Paper

Reference 8

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verified exact
local_arxiv, observed 2026-05-20T13:33:19.017346Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 03814a17-1310-4635-9ea9-06e96df77599 · inbound

Deep Neural Networks for Heavy Lepton-Flavor-Violating Higgs Searches at the LHC cites this paper.

Deep Neural Networks for Heavy Lepton-Flavor-Violating Higgs Searches at the LHC Machine Learning in High Energy Physics Community White Paper

Reference 23

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verified exact
local_arxiv, observed 2026-05-22T06:24:40.689783Z

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Matrix element method at NLO: A fine proof of concept in POWHEG cites this paper.

Matrix element method at NLO: A fine proof of concept in POWHEG Machine Learning in High Energy Physics Community White Paper

Reference 9

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verified exact
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Observation 73200365-c2ed-4797-a54d-a62a8125ecf6 · inbound

EasyScan_HEP 2: Agent-Ready Parameter Scans for High-Energy Physics cites this paper.

EasyScan_HEP 2: Agent-Ready Parameter Scans for High-Energy Physics Machine Learning in High Energy Physics Community White Paper

Reference 1

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local_arxiv, observed 2026-07-01T10:45:42.242936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 637f32ee-1e23-49a4-81be-ceecdcb78e60 · inbound

Heavy-Flavor Electron Classification Using Hadronic Environment as Point Cloud cites this paper.

Heavy-Flavor Electron Classification Using Hadronic Environment as Point Cloud Machine Learning in High Energy Physics Community White Paper

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation f2820768-3c12-41bb-9216-09bb964c2716 · inbound

Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders cites this paper.

Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders Machine Learning in High Energy Physics Community White Paper

Reference 36

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Observation c1cabc30-0cbc-4b15-b256-5fdc514e5005 · inbound

A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation cites this paper.

A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation Machine Learning in High Energy Physics Community White Paper

Reference 1

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Observation 7b9114df-8d04-4ce2-86ce-cf3b6fdd2894 · inbound

Learning transferable event representations for charmed baryon physics at BESIII cites this paper.

Learning transferable event representations for charmed baryon physics at BESIII Machine Learning in High Energy Physics Community White Paper

Reference 1

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