Paper Citation Record · LEDGER

Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2103.06995.

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

pith.paper-citation-record.v1
2103.06995 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:39:57.547350Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T19:38:53.027199Z

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 bf332614-2891-451d-a6eb-0376e3f7c5c4 · inbound

Track reconstruction as a service for collider physics cites this paper.

Track reconstruction as a service for collider physics Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:58.161207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72a753b4-162d-41d3-bfe3-bd41e24f5c55 · inbound

Physics and Computing Performance of the EggNet Tracking Pipeline cites this paper.

Physics and Computing Performance of the EggNet Tracking Pipeline Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking

Reference 4

Resolution: malformed identifier; no resolver link, observed 2026-08-07T11:09:18.881330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfc62f65-fbd6-44d9-8ab8-6475ba00faae · inbound

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments cites this paper.

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T17:24:38.148743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 641a1669-ecf9-41c0-a821-cca614171c3e · inbound

Transformer-Based Approach to Enhance Positron Tracking Performance in MEG II cites this paper.

Transformer-Based Approach to Enhance Positron Tracking Performance in MEG II Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking

Reference 11

Resolution: malformed identifier; no resolver link, observed 2026-08-03T14:45:02.471564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 996989d9-3f00-4040-8ba7-80a156914698 · inbound

Local Conformal Predictions for Calibrated Surrogates cites this paper.

Local Conformal Predictions for Calibrated Surrogates Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking

Reference 260

Resolution: verified exact; arxiv_id, observed 2026-07-03T19:38:53.028950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a3048289-9db0-4d3a-9810-71d19158d2d7 · inbound

Learning Standard Model structure from LHC data with Riemannian flow matching cites this paper.

Learning Standard Model structure from LHC data with Riemannian flow matching Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:36.313713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f811ad22-e3cf-4072-945f-4eac664f946c · inbound

Generative Amplification with Surrogate Monte Carlo cites this paper.

Generative Amplification with Surrogate Monte Carlo Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking

Reference 248

Resolution: unresolved; no resolver link, observed 2026-08-15T14:39:57.547350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.