Paper Citation Record · LEDGER

MadMiner: Machine learning-based inference for particle physics

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:1907.10621.

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

pith.paper-citation-record.v1
1907.10621 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

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

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:12:51.991442Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T06:37:42.840821Z

Reference resolution

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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 0d9b6796-3c85-47f5-841c-24326092ad0c · inbound

Towards a generic implementation of matrix-element maximisation as a classifier in particle physics cites this paper.

Towards a generic implementation of matrix-element maximisation as a classifier in particle physics MadMiner: Machine learning-based inference for particle physics

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T13:25:41.292156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01970d13-2e47-4c5c-9094-1ad270d94aa2 · inbound

Benchmarking simplified template cross sections in $WH$ production cites this paper.

Benchmarking simplified template cross sections in $WH$ production MadMiner: Machine learning-based inference for particle physics

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-14T12:38:02.461946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6aa04470-d253-49f7-8373-a237f8d076e5 · inbound

Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates cites this paper.

Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates MadMiner: Machine learning-based inference for particle physics

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T11:56:59.990597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bc78fcc-8139-4c29-8c66-0753a92ab033 · inbound

Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning cites this paper.

Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning MadMiner: Machine learning-based inference for particle physics

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-14T05:10:13.476420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d96778e-a586-48de-86dd-d856da97d5b8 · inbound

Measurement of off-shell Higgs boson production in the $H^*\rightarrow ZZ\rightarrow 4\ell$ decay channel using a neural simulation-based inference technique in 13 TeV $pp$ collisions with the ATLAS detector cites this paper.

Measurement of off-shell Higgs boson production in the $H^*\rightarrow ZZ\rightarrow 4\ell$ decay channel using a neural simulation-based inference technique in 13 TeV $pp$ collisions with the ATLAS detector MadMiner: Machine learning-based inference for particle physics

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T04:24:20.994039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1c4c0d2-a5b2-4656-9b74-cf3c1684a5c9 · inbound

Fingerprinting New Physics with Effective Field Theories cites this paper.

Fingerprinting New Physics with Effective Field Theories MadMiner: Machine learning-based inference for particle physics

Reference 112

Resolution: unresolved; no resolver link, observed 2026-08-10T20:24:14.670895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05c343d8-07af-48fe-8b50-38c4fcfc8d42 · inbound

Communicating Likelihoods with Normalising Flows cites this paper.

Communicating Likelihoods with Normalising Flows MadMiner: Machine learning-based inference for particle physics

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T21:21:44.396225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9600727b-a920-4aa5-8af3-b5dbea9e4617 · inbound

Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs production cites this paper.

Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs production MadMiner: Machine learning-based inference for particle physics

Reference 37

Resolution: malformed identifier; arxiv_id, observed 2026-05-21T23:30:45.740451Z

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 b5adb4a6-f3f6-4c31-8776-21f2923c11af · inbound

Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties cites this paper.

Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties MadMiner: Machine learning-based inference for particle physics

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T13:28:03.631172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b63f4599-10fe-4819-b9b5-a1e7ccd594c9 · inbound

Unbinning global LHC analyses cites this paper.

Unbinning global LHC analyses MadMiner: Machine learning-based inference for particle physics

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T05:29:30.480494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bcc9ca0-12ef-4a07-9c77-bb97edb09e63 · inbound

Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators cites this paper.

Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators MadMiner: Machine learning-based inference for particle physics

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:11:19.169881Z

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 a12b66a1-9e87-4d74-88ec-614726e4a526 · inbound

AI-Driven Discovery of Information-Efficient Collider Observables for Interference Measurements cites this paper.

AI-Driven Discovery of Information-Efficient Collider Observables for Interference Measurements MadMiner: Machine learning-based inference for particle physics

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-30T20:35:02.979029Z

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 2f949c13-14fc-47a1-b653-6c38b0a9379f · inbound

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 MadMiner: Machine learning-based inference for particle physics

Reference 90

Resolution: verified exact; arxiv_id, observed 2026-07-03T06:37:42.842720Z

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 9266eeb5-8cbb-4577-ac38-abd84f244568 · inbound

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust cites this paper.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust MadMiner: Machine learning-based inference for particle physics

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T00:12:51.991442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.