Paper Citation Record · LEDGER

Energy Flow Networks: Deep Sets for Particle Jets

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:1810.05165.

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

pith.paper-citation-record.v1
1810.05165 v2

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measured 22 of 22 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:19:58.163039Z

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Source: pith, observed 2026-07-10T11:57:03.329024Z

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Pith citing papers

Observation 645f29e3-d378-43f3-8a51-a553335a8f7b · inbound

Looking inside jets: an introduction to jet substructure and boosted-object phenomenology cites this paper.

Looking inside jets: an introduction to jet substructure and boosted-object phenomenology Energy Flow Networks: Deep Sets for Particle Jets

Reference 155

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Observation 97625f5c-52a2-4b2e-a775-bacba26a30c6 · inbound

JEDI-net: a jet identification algorithm based on interaction networks cites this paper.

JEDI-net: a jet identification algorithm based on interaction networks Energy Flow Networks: Deep Sets for Particle Jets

Reference 14

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Observation bf01b144-9097-4a7a-be8a-da4b92967030 · inbound

Exploring the Space of Jets with CMS Open Data cites this paper.

Exploring the Space of Jets with CMS Open Data Energy Flow Networks: Deep Sets for Particle Jets

Reference 45

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Observation eadc8df4-583d-4ff6-84e5-65be4282ace9 · inbound

Transformer networks for Heavy flavor jet tagging cites this paper.

Transformer networks for Heavy flavor jet tagging Energy Flow Networks: Deep Sets for Particle Jets

Reference 92

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Observation 09205545-5e75-4d56-961a-7aa87b0742af · inbound

Neural Architecture Codesign for Fast Physics Applications cites this paper.

Neural Architecture Codesign for Fast Physics Applications Energy Flow Networks: Deep Sets for Particle Jets

Reference 12

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Observation edea0265-5eed-49d7-84c3-fce09e88ca67 · inbound

Search for new physics in final states with semi-visible jets or anomalous signatures using the ATLAS detector cites this paper.

Search for new physics in final states with semi-visible jets or anomalous signatures using the ATLAS detector Energy Flow Networks: Deep Sets for Particle Jets

Reference 65

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Observation caeda5db-6a28-4d02-9601-f820deaa1e72 · inbound

IAFormer: Interaction-Aware Transformer network for collider data analysis cites this paper.

IAFormer: Interaction-Aware Transformer network for collider data analysis Energy Flow Networks: Deep Sets for Particle Jets

Reference 48

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Observation 8e01b317-7e72-4168-b349-2efedd88773d · inbound

Deep Learning to Improve the Sensitivity of Higgs Pair Searches in the $4b$ Channel at the LHC cites this paper.

Deep Learning to Improve the Sensitivity of Higgs Pair Searches in the $4b$ Channel at the LHC Energy Flow Networks: Deep Sets for Particle Jets

Reference 130

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Observation 237f81a1-85b9-49cc-b993-90dd9ec8d8ab · inbound

Graph theory inspired anomaly detection at the LHC cites this paper.

Graph theory inspired anomaly detection at the LHC Energy Flow Networks: Deep Sets for Particle Jets

Reference 104

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Observation 4de62ae4-02a2-4dfd-b1f3-58ef8809d20b · inbound

High-Dimensional Unfolding in Large Backgrounds cites this paper.

High-Dimensional Unfolding in Large Backgrounds Energy Flow Networks: Deep Sets for Particle Jets

Reference 98

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Observation bfad2218-aafb-4313-a19e-106af2886435 · inbound

Amplitude Uncertainties Everywhere All at Once cites this paper.

Amplitude Uncertainties Everywhere All at Once Energy Flow Networks: Deep Sets for Particle Jets

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-05-18T19:21:47.007256Z

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Observation f60d1aa5-e31d-47a2-bd4b-23e20d861f3a · 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 Energy Flow Networks: Deep Sets for Particle Jets

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-17T01:08:47.516437Z

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Observation d2697bfd-122b-4a01-9fa2-228d2579304a · 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 Energy Flow Networks: Deep Sets for Particle Jets

Reference 26

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Observation 7754598d-5875-422b-ad27-3b6974643bb9 · inbound

The PYTHIA Facility cites this paper.

The PYTHIA Facility Energy Flow Networks: Deep Sets for Particle Jets

Reference 95

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Observation 2ba9f03f-012e-4bd8-83d2-77318d684785 · inbound

Kitchen Sink Anomaly Detection cites this paper.

Kitchen Sink Anomaly Detection Energy Flow Networks: Deep Sets for Particle Jets

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:06:04.238507Z

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Observation e4a08968-1546-4934-b9fc-632fbfe738da · inbound

Higgs Physics with the XFEL Compton $\boldsymbol{\gamma\gamma}$ Collider Concept at $\boldsymbol{\sqrt{s}=125}$ GeV cites this paper.

Higgs Physics with the XFEL Compton $\boldsymbol{\gamma\gamma}$ Collider Concept at $\boldsymbol{\sqrt{s}=125}$ GeV Energy Flow Networks: Deep Sets for Particle Jets

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-19T21:33:13.638035Z

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Observation 42e32cef-25e0-47fc-95f5-b5444525f03c · inbound

Higher-order effects in amplitude-assisted polarisation extraction with machine-learning techniques cites this paper.

Higher-order effects in amplitude-assisted polarisation extraction with machine-learning techniques Energy Flow Networks: Deep Sets for Particle Jets

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-07-02T09:56:51.391828Z

Source-reported events for the cited work

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Observation dfaaf3c9-4d45-4f26-8a7f-1b704a1a8a64 · inbound

Transformer-based machine learning using low-level calorimeter signals for collimated photon identification at collider experiments cites this paper.

Transformer-based machine learning using low-level calorimeter signals for collimated photon identification at collider experiments Energy Flow Networks: Deep Sets for Particle Jets

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-07-10T11:57:03.330425Z

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 6fad5ef1-d5f9-42a7-8a70-94ff050e4c8e · inbound

Predict before you train: Scaling Laws for particle physics foundation models cites this paper.

Predict before you train: Scaling Laws for particle physics foundation models Energy Flow Networks: Deep Sets for Particle Jets

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-30T23:56:38.579260Z

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Observation 02a457f9-1cc3-4e7e-9e9f-8e99c28f44fa · inbound

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

Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders Energy Flow Networks: Deep Sets for Particle Jets

Reference 102

Resolution: unresolved; no resolver link, observed 2026-07-31T05:57:09.277678Z

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Observation fb5948e0-7037-410d-8d5a-23d9195709e9 · inbound

String theory mathematics and matrix data analysis cites this paper.

String theory mathematics and matrix data analysis Energy Flow Networks: Deep Sets for Particle Jets

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:54.579135Z

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Observation db4fbcbe-7ad6-4f39-8606-076865f7d80e · inbound

Machine Learning is Good for Physics - and Vice Versa cites this paper.

Machine Learning is Good for Physics - and Vice Versa Energy Flow Networks: Deep Sets for Particle Jets

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T23:04:30.307450Z

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