Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T16:29:15.135471Z
Paper Citation Record · LEDGER
Theory-informed neural networks for particle physics
As of 11 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-02T09:53:40.367066Z
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
- parse uncertain0
- malformed identifier5
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- malformed identifier
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:29:11.382384Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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 2026-08-11T06:34:44.6726+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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.