Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Towards a method to anticipate dark matter signals with deep learning at the LHC
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2105.12018.
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 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T14:39:56.984567Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T19:38:52.972638Z
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 5955556e-6893-460d-9b25-c76d58df0e61 · inbound
Local Conformal Predictions for Calibrated Surrogates cites this paper.
Local Conformal Predictions for Calibrated Surrogates Towards a method to anticipate dark matter signals with deep learning at the LHC
Reference 137
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T19:38:52.974141Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 88d10208-d27e-4386-9dcf-2eec1f956b08 · inbound
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 Towards a method to anticipate dark matter signals with deep learning at the LHC
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T04:54:13.084275Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e8b71bbe-3318-45bc-a51f-496b0a01adba · inbound
Generative Amplification with Surrogate Monte Carlo cites this paper.
Generative Amplification with Surrogate Monte Carlo Towards a method to anticipate dark matter signals with deep learning at the LHC
Reference 125
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T14:39:56.984567Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.