Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Unbinned multivariate observables for global SMEFT analyses from machine learning
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2211.02058.
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 2026-08-10T20:24:14.361727Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-13T21:13:16.618534Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation afa67809-fc02-49f3-87fb-cf5ec58a9b8a · inbound
Fingerprinting New Physics with Effective Field Theories cites this paper.
Fingerprinting New Physics with Effective Field Theories Unbinned multivariate observables for global SMEFT analyses from machine learning
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T20:24:14.361727Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ed859c13-0c7b-4ed0-87bc-a78f4cddc1aa · inbound
Unbinning global LHC analyses cites this paper.
Unbinning global LHC analyses Unbinned multivariate observables for global SMEFT analyses from machine learning
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T05:29:30.548788Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5c8d053d-4769-4725-943f-4ceaf1b6752f · inbound
Many Wrongs Make a Right: Leveraging Biased Simulations Towards Unbiased Parameter Inference cites this paper.
Many Wrongs Make a Right: Leveraging Biased Simulations Towards Unbiased Parameter Inference Unbinned multivariate observables for global SMEFT analyses from machine learning
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T21:13:16.619913Z
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.