Paper Citation Record · LEDGER

Theory-informed neural networks for particle physics

As of 9 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2507.13447.

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

pith.paper-citation-record.v1
2507.13447 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:29:15.135471Z

measured 40 of 40 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T09:53:40.367066Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T09:56:51.245191Z

Reference resolution

39 of 39 outbound references displayed

verified exact9
verified fuzzy1
unresolved23
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External citation measurements

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Outbound references

Observation e67211e4-e45b-4993-beb8-70a87d4ff6d9 · outbound

This paper cites Dynamical Likelihood Method for Reconstruction of Events with Missing Momentum. I. Method and Toy Models.

Theory-informed neural networks for particle physics Dynamical Likelihood Method for Reconstruction of Events with Missing Momentum. I. Method and Toy Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:10.055608Z

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Observation cc4b5dce-7596-4bfb-bec7-fc5de912e4d2 · outbound

This paper cites A precision measurement of the mass of the top quark.

Theory-informed neural networks for particle physics A precision measurement of the mass of the top quark

Reference 2

Resolution: verified exact; doi, observed 2026-08-06T16:29:16.056876Z

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Observation 0e26f53c-3070-497d-9628-ee35cca9bba1 · outbound

This paper cites Finding physics signals with shower deconstruction.

Theory-informed neural networks for particle physics Finding physics signals with shower deconstruction

Reference 3

Resolution: malformed identifier; no resolver link, observed 2026-08-06T16:29:10.306467Z

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Observation c46a3f64-6c5f-4e50-86b7-12f4c5df479f · outbound

This paper cites Finding top quarks with shower deconstruction.

Theory-informed neural networks for particle physics Finding top quarks with shower deconstruction

Reference 4

Resolution: malformed identifier; no resolver link, observed 2026-08-06T16:29:10.438296Z

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Observation 78ac0dcf-88cf-4022-8e63-83c6898dab13 · outbound

This paper cites Finding physics signals with event deconstruction.

Theory-informed neural networks for particle physics Finding physics signals with event deconstruction

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:29:18.144417Z

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Observation 1b36353f-2ee0-47f9-a41a-27ae08997400 · outbound

This paper cites Determining the Structure of Higgs Couplings at the LHC.

Theory-informed neural networks for particle physics Determining the Structure of Higgs Couplings at the LHC

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:10.752431Z

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Observation 8964e66b-ae4a-4e19-b74c-a743cc331ddb · outbound

This paper cites Weighing Wimps with Kinks at Colliders: Invisible Particle Mass Measurements from Endpoints.

Theory-informed neural networks for particle physics Weighing Wimps with Kinks at Colliders: Invisible Particle Mass Measurements from Endpoints

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:29:17.832704Z

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Observation 355a56d6-6bed-431b-a2fe-811b6b9f4b51 · outbound

This paper cites Jet substructure as a new Higgs search channel at the LHC.

Theory-informed neural networks for particle physics Jet substructure as a new Higgs search channel at the LHC

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:11.055398Z

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Observation 44755dff-065a-471a-ba75-2a11e79e9120 · outbound

This paper cites Fat Jets for a Light Higgs.

Theory-informed neural networks for particle physics Fat Jets for a Light Higgs

Reference 9

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Observation 470afe23-a462-41de-97d9-abd94950beb6 · outbound

This paper cites Playing Tag with ANN: Boosted Top Identification with Pattern Recognition.

Theory-informed neural networks for particle physics Playing Tag with ANN: Boosted Top Identification with Pattern Recognition

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:11.257735Z

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Observation c748a67d-2f33-42e9-8106-829c3b79ac66 · outbound

This paper cites Jet-Images -- Deep Learning Edition.

Theory-informed neural networks for particle physics Jet-Images -- Deep Learning Edition

Reference 11

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Observation 58f44c72-b632-4997-ac7b-671d1dd63366 · outbound

This paper cites Parameterized Machine Learning for High-Energy Physics.

Theory-informed neural networks for particle physics Parameterized Machine Learning for High-Energy Physics

Reference 12

Resolution: malformed identifier; no resolver link, observed 2026-08-06T16:29:11.530187Z

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Observation 54bbbdce-72fa-488b-bfd2-63d12e7c23e9 · outbound

This paper cites Mapping Machine-Learned Physics into a Human-Readable Space.

Theory-informed neural networks for particle physics Mapping Machine-Learned Physics into a Human-Readable Space

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:11.630871Z

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Observation 3989a919-4c32-45b1-91f0-aa516b528728 · outbound

This paper cites On the Problem of the Most Efficient Tests of Statistical Hypotheses.

Theory-informed neural networks for particle physics On the Problem of the Most Efficient Tests of Statistical Hypotheses

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:11.748088Z

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Observation 3c232322-7295-495e-9ece-f54ffa66dde3 · outbound

This paper cites Interpretable deep learning models for the inference and classification of LHC data.

Theory-informed neural networks for particle physics Interpretable deep learning models for the inference and classification of LHC data

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:11.865569Z

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Observation da476b14-1dfe-489f-977c-af28d5fa4a3c · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Theory-informed neural networks for particle physics Playing Atari with Deep Reinforcement Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:11.975190Z

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Observation 09ea8f24-c584-4a8e-a130-aa9a3fee818c · outbound

This paper cites Human-level control through deep reinforcement learning.

Theory-informed neural networks for particle physics Human-level control through deep reinforcement learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:12.134048Z

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Observation b6ca44c5-1b47-4cbf-bdcf-fc8c86731f20 · outbound

This paper cites Q-learning.

Theory-informed neural networks for particle physics Q-learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:12.295413Z

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Observation 76f08680-9cb6-496f-9079-0ed377c790e6 · outbound

This paper cites Sutton and Andrew G.

Theory-informed neural networks for particle physics Sutton and Andrew G

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:29:18.501946Z

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 db288b32-25a3-4f3f-ad9d-76ff5ba13a9d · outbound

This paper cites The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations.

Theory-informed neural networks for particle physics The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:12.586616Z

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Observation 8df51760-b7c4-47b1-8f99-d5b0ea28213f · outbound

This paper cites Auto-Encoding Variational Bayes.

Theory-informed neural networks for particle physics Auto-Encoding Variational Bayes

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:12.697428Z

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Observation 988294cc-eead-424d-ada0-18f40872158e · outbound

This paper cites Searching for New Physics with Deep Autoencoders.

Theory-informed neural networks for particle physics Searching for New Physics with Deep Autoencoders

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:12.833137Z

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Observation e370b7a4-1736-4d4e-a86c-cf22a979fb0c · outbound

This paper cites QCD or What?.

Theory-informed neural networks for particle physics QCD or What?

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:13.001985Z

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Observation 7c571fb7-8953-458f-9563-b43c0c06cb12 · outbound

This paper cites Adversarially-trained autoencoders for robust unsupervised new physics searches.

Theory-informed neural networks for particle physics Adversarially-trained autoencoders for robust unsupervised new physics searches

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:13.129824Z

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Observation c18f7fb2-a749-4301-b497-e3c31421cb8d · outbound

This paper cites Anomaly Detection under Coordinate Transformations.

Theory-informed neural networks for particle physics Anomaly Detection under Coordinate Transformations

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:29:17.436876Z

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

Observation 8992cd04-e212-4d06-bdb3-bcde1e79089a · outbound

This paper cites Solving differential equations with neural networks: Applications to the calculation of cosmological phase transitions.

Theory-informed neural networks for particle physics Solving differential equations with neural networks: Applications to the calculation of cosmological phase transitions

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:29:17.193098Z

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Observation 93eadf90-2ed8-4590-9085-b3211e538b65 · outbound

This paper cites Symmetries, Safety, and Self-Supervision.

Theory-informed neural networks for particle physics Symmetries, Safety, and Self-Supervision

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:13.478536Z

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Observation c3fac3d7-bda6-4351-8ca5-bdcc78a5dddb · outbound

This paper cites Self-supervised Anomaly Detection for New Physics.

Theory-informed neural networks for particle physics Self-supervised Anomaly Detection for New Physics

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:29:16.926596Z

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

Observation be4d8dfe-2415-4e87-9703-2de008bd020f · outbound

This paper cites Anomalies, Representations, and Self-Supervision.

Theory-informed neural networks for particle physics Anomalies, Representations, and Self-Supervision

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:13.701416Z

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Observation ae0bdcae-78db-4e8e-9ed4-d35d7eaee760 · outbound

This paper cites A normalized autoencoder for LHC triggers.

Theory-informed neural networks for particle physics A normalized autoencoder for LHC triggers

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:13.853465Z

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Observation 1c5930cb-5d6f-45a6-8233-208b66cb1dd8 · outbound

This paper cites IRC-Safe Graph Autoencoder for Unsupervised Anomaly Detection.

Theory-informed neural networks for particle physics IRC-Safe Graph Autoencoder for Unsupervised Anomaly Detection

Reference 31

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:29:16.624134Z

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

Observation 08181e30-4183-4612-83ca-59557478defa · outbound

This paper cites Anomaly Awareness.

Theory-informed neural networks for particle physics Anomaly Awareness

Reference 32

Resolution: verified exact; doi, observed 2026-08-06T16:29:15.784353Z

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Observation a48bd8b4-2660-4a6b-b58b-e046954fe553 · outbound

This paper cites Uncovering latent jet substructure.

Theory-informed neural networks for particle physics Uncovering latent jet substructure

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:14.452744Z

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Observation 0893127e-59ba-4eb2-929a-c6eb56a03131 · outbound

This paper cites Learning the latent structure of collider events.

Theory-informed neural networks for particle physics Learning the latent structure of collider events

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:14.568572Z

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Observation 8ff1d81d-c05e-48b5-a2e2-390d6059900d · outbound

This paper cites Better Latent Spaces for Better Autoencoders.

Theory-informed neural networks for particle physics Better Latent Spaces for Better Autoencoders

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:14.673057Z

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Observation fc4534f8-45d6-4205-a01b-985f04717b1f · outbound

This paper cites Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure.

Theory-informed neural networks for particle physics Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:29:16.303377Z

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

Observation e19b8b15-5c8d-405f-8637-cc7a64415cd1 · outbound

This paper cites Generator Based Inference (GBI).

Theory-informed neural networks for particle physics Generator Based Inference (GBI)

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:14.997432Z

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Observation 8ee0973b-14d1-4717-b2de-7ec4ab220fb5 · outbound

This paper cites Lorentz group equivariant autoencoders.

Theory-informed neural networks for particle physics Lorentz group equivariant autoencoders

Reference 38

Resolution: malformed identifier; no resolver link, observed 2026-08-06T16:29:15.135471Z

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Observation d823a31a-abe5-4b00-ba1e-a6b7a6b25899 · outbound

This paper cites an unresolved cited work.

Theory-informed neural networks for particle physics Unresolved cited work

Reference 53

Resolution: verified exact; doi, observed 2026-08-06T16:29:15.436337Z

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Pith citing papers

Observation 8f9f940d-e19d-4af3-bbdc-800fbb9b63f5 · 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 Theory-informed neural networks for particle physics

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-02T09:56:51.246435Z

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