Pith. sign in

Paper Citation Record · LEDGER

Improving tracking algorithms with machine learning: a case for line-segment tracking at the High Luminosity LHC

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.13166.

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

pith.paper-citation-record.v1
2403.13166 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:30:19.435408Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T17:14:59.518122Z

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 d2e11382-fe2d-416e-a760-8dbefae94c22 · inbound

Transformer networks for Heavy flavor jet tagging cites this paper.

Transformer networks for Heavy flavor jet tagging Improving tracking algorithms with machine learning: a case for line-segment tracking at the High Luminosity LHC

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-12T18:30:19.435408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:30:19.435408Z digest=sha256:882f7b3f889987666ccd9e1f67d2e8a2ef947351c5b490dd2606a8adfcfd3262

Observation 044b569d-9c77-4477-af57-180316618899 · inbound

IAFormer: Interaction-Aware Transformer network for collider data analysis cites this paper.

IAFormer: Interaction-Aware Transformer network for collider data analysis Improving tracking algorithms with machine learning: a case for line-segment tracking at the High Luminosity LHC

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-22T17:14:59.520287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-22T17:13:47.293753Z digest=sha256:efb4d31ed35e724d7422a3d438c0de965df6f482b1987e23166e44509eb4c517