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Paper Citation Record · LEDGER

Autoencoders for unsupervised anomaly detection in high energy physics

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2104.09051.

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

pith.paper-citation-record.v1
2104.09051 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:19:24.499761Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T19:58:54.588223Z

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 aea7daf5-c476-41e9-9344-70494a8ca1bc · inbound

Enhancing anomaly detection with topology-aware autoencoders cites this paper.

Enhancing anomaly detection with topology-aware autoencoders Autoencoders for unsupervised anomaly detection in high energy physics

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T19:19:24.499761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T19:19:24.499761Z digest=sha256:354821e9a510878cf40b65fc01107cd9fd824b80caa3d4493a368e041c7590c6

Observation c67f701e-81cb-4686-9c59-d73410f0e022 · inbound

Wasserstein normalized autoencoder for anomaly detection cites this paper.

Wasserstein normalized autoencoder for anomaly detection Autoencoders for unsupervised anomaly detection in high energy physics

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-04T12:47:51.561468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T12:47:51.561468Z digest=sha256:3f1b55236b7981546587aae05c069fa35b62e69e58839291a0cca6ae3585ae89

Observation 3921fd84-ba74-4dba-a675-caf4b120d137 · inbound

Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach cites this paper.

Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach Autoencoders for unsupervised anomaly detection in high energy physics

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-11T00:15:51.661047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T18:38:36.907681Z digest=sha256:8927ad8d0f1e2d6e098b49ee83512fbee60dd435bdbfbd8b494c6d52667072c8

Observation 1c945164-1b1a-4a9d-8707-3b1dc2dc98e8 · inbound

Local Conformal Predictions for Calibrated Surrogates cites this paper.

Local Conformal Predictions for Calibrated Surrogates Autoencoders for unsupervised anomaly detection in high energy physics

Reference 225

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T19:58:54.589794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-07-03T19:29:34.070294Z digest=sha256:945555840a349b27d5b09732efb6943ff4a6392001e7006a7f35975888b7efe5

Observation 4f54fd4a-6555-4d38-80e7-c3c878f8b152 · inbound

Towards anomaly detection searches for new physics signatures including Higgs bosons with weakly supervised machine learning cites this paper.

Towards anomaly detection searches for new physics signatures including Higgs bosons with weakly supervised machine learning Autoencoders for unsupervised anomaly detection in high energy physics

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-01T12:51:49.106539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:51:49.106539Z digest=sha256:46dcdc4a9a23233ba39899e184249c579ba3ec0a27b67ee9b83c7d75e8624623