Paper Citation Record · LEDGER

E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2101.07263.

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

pith.paper-citation-record.v1
2101.07263 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:14:53.089942Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T21:13:16.554988Z

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 d384e06a-8ce0-4e44-aef5-b60075949363 · inbound

High-Dimensional Unfolding in Large Backgrounds cites this paper.

High-Dimensional Unfolding in Large Backgrounds E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T19:14:53.089942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37e8ebb9-fddb-45c3-bc91-61d5275bd0c9 · inbound

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

Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T13:28:03.652591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de714309-7ef7-4679-b1b6-1fd0d03b3229 · 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 E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-13T21:13:16.556288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c32cf430-e5ac-4979-b408-f4bf49715450 · 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 E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:16.741091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.