Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T14:43:10.206604Z
Paper Citation Record · LEDGER
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry
As of 9 August 2026, this Paper Citation Record lists 88 of 88 outbound references and 0 inbound Pith citation observations for arXiv:2507.17881.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
88 of 88 outbound references displayed
- verified exact43
- verified fuzzy8
- unresolved24
- parse uncertain0
- malformed identifier0
- metadata mismatch13
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation e7146c4a-0d20-4847-8086-e381c0aad9c2 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.681423Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6ebbeb25-ea4e-4d9d-b1ee-556e655d2f3f · outbound
This paper cites The pipeline begins with PIC simulations in CST Particle Studio to generate a multipactor susceptibility dataset for six materials.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry The pipeline begins with PIC simulations in CST Particle Studio to generate a multipactor susceptibility dataset for six materials
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:43:14.354654Z
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 5f2221e6-9b6c-44de-8f96-0d5b0759e200 · outbound
This paper cites #. Scores are normalized by the total mutual information across all features such that the bars sum to 1, highlighting each feature’s relative importance in predicting δ!.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry #. Scores are normalized by the total mutual information across all features such that the bars sum to 1, highlighting each feature’s relative importance in predicting δ!
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:43:14.303855Z
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 74e5e186-bce4-4cca-9b7b-9c8f3b0d66c8 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.717378Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 215eb4ed-565d-442e-9984-a97e5930a2e3 · outbound
This paper cites # in the (V$%, f×d) plane (Fig. 1), following the similarity scaling law of Vaughan (1988), where f is the excitation frequency. Regions where δ!.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry # in the (V$%, f×d) plane (Fig. 1), following the similarity scaling law of Vaughan (1988), where f is the excitation frequency. Regions where δ!
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:43:14.399502Z
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 6299cace-1115-4a2f-9cb1-f694ff9735ef · outbound
This paper cites Entropy, Relative Entropy, and Mutual Information,.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Entropy, Relative Entropy, and Mutual Information,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:43:14.330424Z
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 790619d4-d236-4ca0-abf0-68d2bc5d564b · outbound
This paper cites # is shown in the color bar. Regions with δ!.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry # is shown in the color bar. Regions with δ!
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:43:14.220894Z
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 ff683c28-820a-4d67-8bae-28564ebd4053 · outbound
This paper cites Under the leave-one-material-out cross-validation strategy, the held-out material occupies a region in feature space that is entirely disjoint from the training data.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Under the leave-one-material-out cross-validation strategy, the held-out material occupies a region in feature space that is entirely disjoint from the training data
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:43:14.186334Z
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 5ab686ae-0848-4c0d-92cd-3d2a0bd0e8d9 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 18
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T14:43:14.164594Z
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 c8b6251e-bac2-4fe7-bff9-a15c1d4040df · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.821430Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ea7d752b-dd97-4739-a79b-7468c5d7babb · outbound
This paper cites No.99CH36366).
Reference 24
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T14:43:14.041931Z
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 2955f30a-c043-4df4-97cf-5e191455e541 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 31
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:11.415943Z
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 1e344828-78af-42f0-8ebb-ed65f6f6166a · outbound
This paper cites In Search of Lost Domain Generalization.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry In Search of Lost Domain Generalization
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.869386Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0ac4d429-f761-405c-845d-d898a7296acf · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 36
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:11.274277Z
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 bc89910f-a605-4ca1-bae8-e116e3c1a9e1 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 37
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:11.248203Z
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 ae6ea67c-9dab-4349-ae07-2740e58626f6 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 38
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:11.223991Z
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 29ba04e4-5fbe-4967-9722-03d0d602ae5b · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 39
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:11.203082Z
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 9aea05af-5b21-4e99-8b65-02712d45724c · outbound
This paper cites Michigan State University.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Michigan State University
Reference 40
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:11.014429Z
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 956dc63c-9244-4ae3-93fa-a32b7ada0f4c · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 41
- Resolution
- metadata mismatch
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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 7ed35484-27a6-4d1c-9a2c-8b3a4ce388c8 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 43
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ef9f4c08-daa5-4905-9919-5e682bf80be9 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 48
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.929242Z
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 47307638-573f-461c-ba66-5ab3d3a5b3dc · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 49
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.911110Z
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 0a846569-5106-4a40-9444-814ee5315bbb · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 51
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.876300Z
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 55142659-dc7d-4585-8d7f-574a04fb0172 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 52
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.861582Z
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 f2dcb31b-07fe-4588-b4d3-19bfb28cd16d · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 54
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.846794Z
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 5ec8f0ef-b82f-4932-8405-fc0f7d04945c · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 55
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.830179Z
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 c40c9016-c7db-4dc7-8a0c-f7d1482c35af · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 56
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.812947Z
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 d0cfdd3c-e1ba-46c8-9c57-d40d3a898980 · outbound
This paper cites Linac_Gen: integrating machine learning and particle-in-cell methods for enhanced beam dynamics at Fermilab.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Linac_Gen: integrating machine learning and particle-in-cell methods for enhanced beam dynamics at Fermilab
Reference 57
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T14:43:10.791145Z
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 c052392e-392b-4ac6-8fdc-f29d59341d10 · outbound
This paper cites Demonstration of Niobium Tin in 218 MHz Low-beta Quarter Wave Accelerator Cavity.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Demonstration of Niobium Tin in 218 MHz Low-beta Quarter Wave Accelerator Cavity
Reference 58
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T14:43:10.759870Z
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 6e686349-1fa2-4aa3-b5c5-f2085869403d · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 59
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.728861Z
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 644e9b06-ad12-4424-8605-25cd4ff3e074 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 60
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.703890Z
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 6278b0bd-9e73-48b2-89a8-bedaec4cecd3 · outbound
This paper cites AIP Conf.
Reference 61
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.681268Z
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 4ce0923f-2d11-4806-bf56-46b8b7ef9e68 · outbound
This paper cites IRE Trans.
Reference 62
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T14:43:13.232110Z
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 1acee3cf-d8dd-4e00-bd55-40f893803934 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 64
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.645080Z
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 f5ff767a-2413-4845-8de4-a45ac7f41283 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 66
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation edc41f9d-523c-4c47-8158-bbcd31f7672a · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 67
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c2c6b154-df53-41b5-91cf-b23f859c4710 · outbound
This paper cites https://doi.org/10.18429/JACOW-IPAC2024-WEPC60 Space Engineering: Multipacting Design and Test,.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry https://doi.org/10.18429/JACOW-IPAC2024-WEPC60 Space Engineering: Multipacting Design and Test,
Reference 68
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.570138Z
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 c4d8d09d-3114-41a9-8273-7a36e16e5ca2 · outbound
This paper cites Plasma Phys.
Reference 69
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.544609Z
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 37a41e2d-b2f4-495b-b0e7-34de273bbda6 · outbound
This paper cites IEEE Trans.
Reference 70
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T14:43:12.981642Z
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 812b9d26-1cdf-4633-94a3-94a4b8b0c05f · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 71
- Resolution
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- raw_fallback, observed 2026-08-06T14:43:12.844869Z
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 f3c4ecd6-58e4-4775-8eb0-9baf332b7c83 · outbound
This paper cites JACoW IPAC2023, WEPA127.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry JACoW IPAC2023, WEPA127
Reference 72
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.963937Z
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 54493e9d-466d-46e0-a6a4-0686f9e627ed · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 73
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.519835Z
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 bb29a413-85fd-4356-9bad-fc7743021bcf · outbound
This paper cites IEEE Trans.
Reference 75
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.490520Z
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 edc2fde4-2081-4d24-a1fc-5d11d334ab0a · outbound
This paper cites IEEE Trans.
Reference 76
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.456599Z
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 781f4922-b57c-4bb0-a5ee-07b6dfd21ce7 · outbound
This paper cites IRE Trans.
Reference 77
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T14:43:12.591865Z
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 0451d2fc-1859-4147-aa7a-67abc8da5728 · outbound
This paper cites IEEE Trans.
Reference 78
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 77dbff5c-f1da-491d-9153-586afaf0b152 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 80
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T14:43:12.323439Z
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 7d4e9399-d4e6-4802-9983-e2aa0a2c4330 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 81
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T14:43:12.148197Z
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 01a89fb7-6af1-406d-bec4-8d5af93127c5 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 82
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T14:43:12.032224Z
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 c9341ef6-eb05-43c6-8b03-06cc288c60b9 · outbound
This paper cites https://doi.org/10.18429/JACOW-IPAC2024-THPG04 Yang, S., Nam, J., Dietschreit, J.C.B., Gómez-Bombarelli, R.,.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry https://doi.org/10.18429/JACOW-IPAC2024-THPG04 Yang, S., Nam, J., Dietschreit, J.C.B., Gómez-Bombarelli, R.,
Reference 83
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.414766Z
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 473440ec-2710-4f8a-b212-1af119466638 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 84
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.381895Z
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 967a287f-27cc-438d-8151-7b7928b7e867 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 86
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T14:43:11.839548Z
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 ce7af68f-9a30-4d7a-8d9c-54760cb2e507 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 87
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.312790Z
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 52ab31eb-33ff-4cee-9b7a-214a1549a0f2 · outbound
This paper cites Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics
Reference 88
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T14:43:10.278754Z
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 76817116-8a88-46b0-b115-31eda7e6c486 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 89
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:10.206604Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4bd317b6-516d-437f-9410-fecd7d1339a0 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 99
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.660772Z
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 ea06a817-a322-45f6-85e1-e6b29313668e · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 1627
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.892463Z
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 cbaaea00-c4ff-4919-86a0-01e3964aa1aa · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 1954
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:11.301712Z
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 22247be8-6d87-4184-86d1-ab6f4f380203 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 1958
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:11.331836Z
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 a5b159e3-b92d-48d1-97c4-373217ebf3fa · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 1961
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.706020Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 03beca7b-12e7-42dd-b525-048620fa0cef · outbound
This paper cites This highly nonlinear phenomenon is triggered by secondary electron emission (SEE) (Furman and Pivi, 2002; Iqbal et al., 2019; Ludwick et al., 2020; Vaughan, 1989,.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry This highly nonlinear phenomenon is triggered by secondary electron emission (SEE) (Furman and Pivi, 2002; Iqbal et al., 2019; Ludwick et al., 2020; Vaughan, 1989,
Reference 1988
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.687440Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e6b2ea68-56b7-47f6-a48b-e8580bf366a9 · outbound
This paper cites 5 Table 1: Secondary electron yield (SEY) parameters for the six material property sets used in this study.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry 5 Table 1: Secondary electron yield (SEY) parameters for the six material property sets used in this study
Reference 1989
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:43:14.376152Z
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 531a83e3-3d28-4b8e-b374-710b1be7c7e0 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 1993
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.693966Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9f84599-69aa-4ede-a795-35dc31a3abf5 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 1996
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T14:43:14.272606Z
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 4f107248-0c52-49bb-9756-95cd46920ad1 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 1997
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T14:43:12.704361Z
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 7012c55e-81b7-4cb0-a2dd-f4f97ff2f44c · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 1998
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.946410Z
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 0f8e1404-52f1-40b2-b475-fa8c9884edf7 · outbound
This paper cites XGBoost is a scalable, regularized boosting algorithm that sequentially fits new decision trees to correct the residual errors of previous trees.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry XGBoost is a scalable, regularized boosting algorithm that sequentially fits new decision trees to correct the residual errors of previous trees
Reference 1999
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:43:14.242307Z
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 f9b1f908-d79c-4315-a064-11e72e61f8c9 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 2001
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.816686Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aedb839d-cbf3-427a-8677-36797dc64e52 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 2002
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.852618Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cafb1f5c-6b6f-4940-b923-cc65c791d0b4 · outbound
This paper cites Extremal Systems of Convex Sets with Applications to Convex Calculus in Vector Spaces.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Extremal Systems of Convex Sets with Applications to Convex Calculus in Vector Spaces
Reference 2003
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T14:43:13.270009Z
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 185a281f-8b11-4794-8864-f155fd1e90f9 · outbound
This paper cites Radiophys.
Reference 2004
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T14:43:12.461814Z
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 67815788-3f8c-4699-8422-56110aa81386 · outbound
This paper cites John Wiley & Sons, Ltd, pp.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry John Wiley & Sons, Ltd, pp
Reference 2005
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.847636Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d8838d42-b094-4e96-8b65-9b304913cfaa · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 2006
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.856672Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a1483092-7127-4ae5-8605-6f0f5c0f4a64 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 2010
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:11.633921Z
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 0e26544e-6765-4a2f-8178-2b12349d06e0 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 2011
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.349887Z
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 55c477b4-96f3-4a53-9389-df4d46162d7d · outbound
This paper cites IEEE Trans.
Reference 2012
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T14:43:14.138659Z
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 4344462b-6b92-4876-8213-24581c1601a1 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 2013
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.997347Z
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 919a8913-d1df-45b3-9933-b0985246d857 · outbound
This paper cites Association for Computing Machinery, New York, NY, USA, pp.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Association for Computing Machinery, New York, NY, USA, pp
Reference 2016
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.832312Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ba7a5d9d-cd2a-486d-8454-88766e0c19ef · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 2017
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.699650Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 95903c7c-da21-46ea-872d-e14043fc55aa · outbound
This paper cites Opportunities in Machine Learning for Particle Accelerators.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Opportunities in Machine Learning for Particle Accelerators
Reference 2018
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T14:43:11.544386Z
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 33307a84-a643-42f4-b4d3-838bac3caadf · outbound
This paper cites Special issue,.
Reference 2019
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.726229Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eabea2c1-e402-4f4d-9407-961fdae42f50 · outbound
This paper cites Invariant Risk Minimization.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Invariant Risk Minimization
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.811041Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e3211ce0-4a0d-4ec4-b7f9-0dd80fa3a94c · outbound
This paper cites Fusion Eng.
Reference 2021
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T14:43:13.788631Z
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 58d2c046-c59d-414e-8656-9cd6fbbb897e · outbound
This paper cites CST Studio Suite,.
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.732215Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c43384d5-5a95-4b23-b4ef-c17d973496e3 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 2023
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T14:43:13.464723Z
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 3c8fda51-bcbd-4c9b-b1ff-f2aa7c795bcb · outbound
This paper cites Results Phys.
Reference 2024
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T14:43:13.669917Z
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 638f93ce-fb28-4958-8967-0f3e9dc49d6d · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 2025
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:43:09.935580Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7aa5d706-c62b-43b3-87ae-914d2afab113 · outbound
This paper cites an unresolved cited work.
A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work
Reference 2752
- Resolution
- verified exact
- doi, observed 2026-08-06T14:43:10.623216Z
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.
Pith citing papers
No inbound Pith citation observations are available.