Paper Citation Record · LEDGER

A Living Review of Machine Learning for Particle Physics

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:2102.02770.

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

pith.paper-citation-record.v1
2102.02770 v1

Coverage vector

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measured 27 of 27 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:59:46.416283Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T19:58:54.496237Z

Reference resolution

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Outbound references

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Pith citing papers

Observation 551f6541-79e8-4583-b400-290b8057b9de · inbound

The S-matrix bootstrap with neural optimizers I: zero double discontinuity cites this paper.

The S-matrix bootstrap with neural optimizers I: zero double discontinuity A Living Review of Machine Learning for Particle Physics

Reference 7

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Observation 78b0a6f6-3c2a-4b67-bd13-476970ef196f · inbound

hep-aid: A Python Library for Sample Efficient Parameter Scans in Beyond the Standard Model Phenomenology cites this paper.

hep-aid: A Python Library for Sample Efficient Parameter Scans in Beyond the Standard Model Phenomenology A Living Review of Machine Learning for Particle Physics

Reference 5

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Observation 2cbe19cf-f9a0-4fa2-8a40-4b8d24b5c569 · inbound

Learning Broken Symmetries with Approximate Invariance cites this paper.

Learning Broken Symmetries with Approximate Invariance A Living Review of Machine Learning for Particle Physics

Reference 3

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Observation 5f649ddd-e23d-484f-832b-4ee5a47f5c35 · inbound

Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics cites this paper.

Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics A Living Review of Machine Learning for Particle Physics

Reference 120

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

A Step Toward Interpretability: Smearing the Likelihood A Living Review of Machine Learning for Particle Physics

Reference 18

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Observation 57de5200-cf88-4ae1-91c7-f63ceba25d35 · inbound

Les Houches 2023 -- Physics at TeV Colliders: Report on the Standard Model Precision Wishlist cites this paper.

Les Houches 2023 -- Physics at TeV Colliders: Report on the Standard Model Precision Wishlist A Living Review of Machine Learning for Particle Physics

Reference 135

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Observation 99247a7a-64e5-4f02-be65-da6470726f81 · inbound

What exactly did the Transformer learn from our physics data? cites this paper.

What exactly did the Transformer learn from our physics data? A Living Review of Machine Learning for Particle Physics

Reference 9

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Observation e1c640f9-8ff1-4662-902e-1f7c14fba828 · inbound

Machine Learning in the 2HDM2S model for Dark Matter cites this paper.

Machine Learning in the 2HDM2S model for Dark Matter A Living Review of Machine Learning for Particle Physics

Reference 54

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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 A Living Review of Machine Learning for Particle Physics

Reference 21

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Electroweak phase transition in SMEFT: Gravitational wave and collider complementarity cites this paper.

Electroweak phase transition in SMEFT: Gravitational wave and collider complementarity A Living Review of Machine Learning for Particle Physics

Reference 33

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Observation 5ff6805f-77ef-42ec-96b2-ec2e8842499d · inbound

Transformer-Based Approach to Enhance Positron Tracking Performance in MEG II cites this paper.

Transformer-Based Approach to Enhance Positron Tracking Performance in MEG II A Living Review of Machine Learning for Particle Physics

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T14:45:02.243410Z

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Observation 43b3932a-35bc-49ce-8602-8721989b8d6d · inbound

OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers cites this paper.

OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers A Living Review of Machine Learning for Particle Physics

Reference 14

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Observation cb785bbe-1cf8-4b16-a1e1-05c059fa1383 · inbound

Explicit or Implicit? Encoding Physics at the Precision Frontier cites this paper.

Explicit or Implicit? Encoding Physics at the Precision Frontier A Living Review of Machine Learning for Particle Physics

Reference 3

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AI and the Research-Education Environment of Physics cites this paper.

AI and the Research-Education Environment of Physics A Living Review of Machine Learning for Particle Physics

Reference 1

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Uncovering Hidden Systematics in Neural Network Models for High Energy Physics cites this paper.

Uncovering Hidden Systematics in Neural Network Models for High Energy Physics A Living Review of Machine Learning for Particle Physics

Reference 1

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Observation 0641620f-d1c1-4909-9c70-efe441de6a02 · inbound

The Monte Carlo Ecosystem in High-Energy Physics: A Primer cites this paper.

The Monte Carlo Ecosystem in High-Energy Physics: A Primer A Living Review of Machine Learning for Particle Physics

Reference 204

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Observation 635e64c9-0cd9-4d30-82e2-cc4176faaa4c · 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 A Living Review of Machine Learning for Particle Physics

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:33:19.076888Z

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Observation cdc09dc6-24eb-49a2-81f0-fbbd8df29ef3 · 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 A Living Review of Machine Learning for Particle Physics

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:24:40.647363Z

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Observation 71f41723-6deb-42d7-a54e-85e36bbbecde · inbound

Symbolic Classification-Enabled LHC Limits Online BSM Global Fits cites this paper.

Symbolic Classification-Enabled LHC Limits Online BSM Global Fits A Living Review of Machine Learning for Particle Physics

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T05:04:37.079153Z

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Observation 22ad05bb-7dcc-451c-b4b9-6ec845a6ce3e · 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 A Living Review of Machine Learning for Particle Physics

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-01T10:45:42.245525Z

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Local Conformal Predictions for Calibrated Surrogates cites this paper.

Local Conformal Predictions for Calibrated Surrogates A Living Review of Machine Learning for Particle Physics

Reference 223

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Observation c041606c-2390-4340-be79-0b4df9f6b26c · inbound

Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough cites this paper.

Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough A Living Review of Machine Learning for Particle Physics

Reference 31

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Observation 564d397d-d1c3-4ead-8aa7-0ece23578f59 · inbound

Learning Standard Model structure from LHC data with Riemannian flow matching cites this paper.

Learning Standard Model structure from LHC data with Riemannian flow matching A Living Review of Machine Learning for Particle Physics

Reference 13

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Observation 08b8465d-2660-42a7-94ed-bf95ffb11ea4 · inbound

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

Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders A Living Review of Machine Learning for Particle Physics

Reference 41

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

Learning transferable event representations for charmed baryon physics at BESIII A Living Review of Machine Learning for Particle Physics

Reference 2

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Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network cites this paper.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network A Living Review of Machine Learning for Particle Physics

Reference 11

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Observation 75cffa26-1d5a-444e-85a0-35b2479fe572 · inbound

The Living Guide of Machine Learning for Particle Physics cites this paper.

The Living Guide of Machine Learning for Particle Physics A Living Review of Machine Learning for Particle Physics

Reference 1

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