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Paper Citation Record · LEDGER

Enhancing anomaly detection with topology-aware autoencoders

As of 20 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 1 inbound Pith citation observation for arXiv:2502.10163.

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

pith.paper-citation-record.v1
2502.10163 v1

Coverage vector

measured 74 of 74 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:19:24.655485Z

measured 75 of 75 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:31:43.153781Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T18:31:43.729205Z

Reference resolution

74 of 74 outbound references displayed

  • verified exact17
  • verified fuzzy4
  • unresolved51
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 66b90693-cb56-4fb8-86d5-2a34ed7e3c14 · outbound

This paper cites Machine Learning for Anomaly Detection in Particle Physics.

Enhancing anomaly detection with topology-aware autoencoders Machine Learning for Anomaly Detection in Particle Physics

Reference 1

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Observation fcecda54-e516-4807-84f2-d3593d4020ec · outbound

This paper cites The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics.

Enhancing anomaly detection with topology-aware autoencoders The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics

Reference 2

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Observation 5879cb3d-7869-4c23-9511-d02383226eac · outbound

This paper cites The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider.

Enhancing anomaly detection with topology-aware autoencoders The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

Reference 3

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Observation 03110f68-73ae-4a9f-818a-a49080cc21d9 · outbound

This paper cites Learning New Physics from a Machine.

Enhancing anomaly detection with topology-aware autoencoders Learning New Physics from a Machine

Reference 4

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source=pdf_text observed=2026-08-07T19:19:23.791781Z digest=sha256:cf32b26af4d29720af7ae1ae6e3c97cfb5863574ce6aef853038046d8faba81a

Observation 769f42e1-a121-4dad-b681-9df654e02393 · outbound

This paper cites Anomaly Detection for Resonant New Physics with Machine Learning.

Enhancing anomaly detection with topology-aware autoencoders Anomaly Detection for Resonant New Physics with Machine Learning

Reference 5

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Observation f9e64f4b-dd13-40fd-b9af-5f71e51fbbce · outbound

This paper cites Extending the Bump Hunt with Machine Learning.

Enhancing anomaly detection with topology-aware autoencoders Extending the Bump Hunt with Machine Learning

Reference 6

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Observation 4b6398ea-a215-4065-9a1d-ec7c75ec6b3e · outbound

This paper cites Novelty Detection Meets Collider Physics.

Enhancing anomaly detection with topology-aware autoencoders Novelty Detection Meets Collider Physics

Reference 7

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Observation 164823dc-0016-432c-af2e-ae9f91db49cd · outbound

This paper cites Guiding New Physics Searches with Unsupervised Learning.

Enhancing anomaly detection with topology-aware autoencoders Guiding New Physics Searches with Unsupervised Learning

Reference 8

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Observation ae44712f-9058-4244-b569-96a60f543bff · outbound

This paper cites Simulation Assisted Likelihood-free Anomaly Detection.

Enhancing anomaly detection with topology-aware autoencoders Simulation Assisted Likelihood-free Anomaly Detection

Reference 9

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Observation 8c5f95d9-48c3-410f-aeca-a390558cf4c1 · outbound

This paper cites Anomaly Detection with Density Estimation.

Enhancing anomaly detection with topology-aware autoencoders Anomaly Detection with Density Estimation

Reference 10

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Observation 71a5a6fb-16a9-47de-a9c9-e3e68ee6d97f · outbound

This paper cites Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark.

Enhancing anomaly detection with topology-aware autoencoders Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark

Reference 11

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Observation feca523f-97fa-40de-9a01-0691e828e9bd · outbound

This paper cites Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector.

Enhancing anomaly detection with topology-aware autoencoders Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector

Reference 12

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Observation 983599d3-8176-4b30-8e92-dce7136c4dde · outbound

This paper cites Learning the latent structure of collider events.

Enhancing anomaly detection with topology-aware autoencoders Learning the latent structure of collider events

Reference 13

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Observation a8293bff-2c9b-494c-97cc-c263aad4ae87 · outbound

This paper cites Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders.

Enhancing anomaly detection with topology-aware autoencoders Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders

Reference 14

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Observation 81b2a170-f13e-440f-90f9-17c6f1d2c368 · outbound

This paper cites Variational Autoencoders for Anomalous Jet Tagging.

Enhancing anomaly detection with topology-aware autoencoders Variational Autoencoders for Anomalous Jet Tagging

Reference 15

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Observation 1905ecac-e232-43db-9c51-03f343672eed · outbound

This paper cites Anomaly Awareness.

Enhancing anomaly detection with topology-aware autoencoders Anomaly Awareness

Reference 16

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Observation 2c11c283-7431-434c-b250-9e169cede161 · outbound

This paper cites Unsupervised clustering for collider physics.

Enhancing anomaly detection with topology-aware autoencoders Unsupervised clustering for collider physics

Reference 17

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local_arxiv, observed 2026-08-07T19:19:26.004337Z

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Observation 83318cc5-958b-43c0-b63b-ab8ccdda1ff1 · outbound

This paper cites Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge.

Enhancing anomaly detection with topology-aware autoencoders Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge

Reference 18

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Observation 8980faa8-2012-4e56-afe2-fd09e6928875 · outbound

This paper cites Unsupervised Event Classification with Graphs on Classical and Photonic Quantum Computers.

Enhancing anomaly detection with topology-aware autoencoders Unsupervised Event Classification with Graphs on Classical and Photonic Quantum Computers

Reference 19

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Observation 6ee1e87d-e91e-4dc9-99d6-3383e23ffbce · outbound

This paper cites RanBox: Anomaly Detection in the Copula Space.

Enhancing anomaly detection with topology-aware autoencoders RanBox: Anomaly Detection in the Copula Space

Reference 20

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Observation bc878217-e505-4124-98ce-0d999f476843 · outbound

This paper cites Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC.

Enhancing anomaly detection with topology-aware autoencoders Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC

Reference 21

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source=pdf_text observed=2026-08-07T19:19:24.142209Z digest=sha256:4755e730c8a8fc1148ff06d2e426ba7f99ab0d9107dfb7d630b344a1fbb384bd

Observation 5e11c570-7fda-4664-a221-6a034a7c6ecc · outbound

This paper cites Classifying Anomalies THrough Outer Density Estimation (CATHODE).

Enhancing anomaly detection with topology-aware autoencoders Classifying Anomalies THrough Outer Density Estimation (CATHODE)

Reference 22

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Observation 13efcf97-33ca-49d6-a4fa-5d82dee9a97e · outbound

This paper cites Online-compatible Unsupervised Non-resonant Anomaly Detection.

Enhancing anomaly detection with topology-aware autoencoders Online-compatible Unsupervised Non-resonant Anomaly Detection

Reference 23

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Observation f0eea13d-c2d6-4af2-93aa-89d3e0b74b99 · outbound

This paper cites Learning New Physics from an Imperfect Machine.

Enhancing anomaly detection with topology-aware autoencoders Learning New Physics from an Imperfect Machine

Reference 24

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Observation 988dbb5f-15a1-4e99-bd41-6957f054d487 · outbound

This paper cites Neural Embedding: Learning the Embedding of the Manifold of Physics Data.

Enhancing anomaly detection with topology-aware autoencoders Neural Embedding: Learning the Embedding of the Manifold of Physics Data

Reference 25

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Observation 29d2e14a-417a-424a-821e-4a0c2d44b6ab · outbound

This paper cites Resonant anomaly detection without background sculpting.

Enhancing anomaly detection with topology-aware autoencoders Resonant anomaly detection without background sculpting

Reference 26

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Observation e39ecdb4-c5ef-4ff2-abdf-e3abad6001d7 · outbound

This paper cites Anomaly Detection under Coordinate Transformations.

Enhancing anomaly detection with topology-aware autoencoders Anomaly Detection under Coordinate Transformations

Reference 27

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Observation ed75d6d5-c5bd-4fa8-a9f2-af868598accc · outbound

This paper cites Lorentz group equivariant autoencoders.

Enhancing anomaly detection with topology-aware autoencoders Lorentz group equivariant autoencoders

Reference 28

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Observation eefde4b0-0c57-4332-8c36-316daa235e57 · outbound

This paper cites The Mass-ive Issue: Anomaly Detection in Jet Physics.

Enhancing anomaly detection with topology-aware autoencoders The Mass-ive Issue: Anomaly Detection in Jet Physics

Reference 29

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local_arxiv, observed 2026-08-07T19:19:25.778787Z

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Observation 68b53c7a-a3d2-4a73-80db-656c5668a476 · outbound

This paper cites Anomaly detection search for new resonances decaying into a Higgs boson and a generic new particle $X$ in hadronic final states using $\sqrt{s} = 13$ TeV $pp$ collisions with the ATLAS detector.

Enhancing anomaly detection with topology-aware autoencoders Anomaly detection search for new resonances decaying into a Higgs boson and a generic new particle $X$ in hadronic final states using $\sqrt{s} = 13$ TeV $pp$ collisions with the ATLAS detector

Reference 30

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 3e5bd52b-7758-4d63-9720-65a7726beaf2 · outbound

This paper cites Anomaly Detection in Collider Physics via Factorized Observables.

Enhancing anomaly detection with topology-aware autoencoders Anomaly Detection in Collider Physics via Factorized Observables

Reference 31

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Observation 515b9a1d-5aa2-4541-8b49-c096591c5af7 · outbound

This paper cites Improving new physics searches with diffusion models for event observables and jet constituents.

Enhancing anomaly detection with topology-aware autoencoders Improving new physics searches with diffusion models for event observables and jet constituents

Reference 32

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source=pdf_text observed=2026-08-07T19:19:24.299250Z digest=sha256:6a1e97d0fca0dc52f32035cb393fe062841560cc962da2fa18e7c63d91033b9a

Observation cd24f30c-c2a5-4e48-a703-805f2b5c5785 · outbound

This paper cites Incorporating Physical Priors into Weakly-Supervised Anomaly Detection.

Enhancing anomaly detection with topology-aware autoencoders Incorporating Physical Priors into Weakly-Supervised Anomaly Detection

Reference 33

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Observation 5b9edb2f-9203-42a3-8ff0-f06b5aa34abe · outbound

This paper cites Anomaly-aware summary statistic from data batches.

Enhancing anomaly detection with topology-aware autoencoders Anomaly-aware summary statistic from data batches

Reference 34

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local_arxiv, observed 2026-08-07T19:19:25.674669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T19:19:24.310575Z digest=sha256:6110a6b411adc64e2b5d11855eeb44937326b9845271b90941498f9e47adfd47

Observation bf5b2a7a-b511-4b15-8a50-402fe4d62e26 · outbound

This paper cites Unsupervised Beyond-Standard-Model Event Discovery at the LHC with a Novel Quantum Autoencoder.

Enhancing anomaly detection with topology-aware autoencoders Unsupervised Beyond-Standard-Model Event Discovery at the LHC with a Novel Quantum Autoencoder

Reference 35

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Observation f08e0f74-8f8c-4e1e-97a0-b2ff62ff1eb2 · outbound

This paper cites SIGMA: Single Interpolated Generative Model for Anomalies.

Enhancing anomaly detection with topology-aware autoencoders SIGMA: Single Interpolated Generative Model for Anomalies

Reference 36

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Observation 75d7a9c7-73b6-4c81-b42c-34e6314a70d1 · outbound

This paper cites Exploring Optimal Transport for Event-Level Anomaly Detection at the Large Hadron Collider.

Enhancing anomaly detection with topology-aware autoencoders Exploring Optimal Transport for Event-Level Anomaly Detection at the Large Hadron Collider

Reference 37

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Observation 3853cc65-9892-46c1-886e-e81df6c1d7a3 · outbound

This paper cites The role of data embedding in quantum autoencoders for improved anomaly detection.

Enhancing anomaly detection with topology-aware autoencoders The role of data embedding in quantum autoencoders for improved anomaly detection

Reference 38

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Observation d0b89679-7c6e-4d25-b247-3cbca269e8e6 · outbound

This paper cites Accurate and robust methods for direct background estimation in resonant anomaly detection.

Enhancing anomaly detection with topology-aware autoencoders Accurate and robust methods for direct background estimation in resonant anomaly detection

Reference 39

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Observation 08341537-4af1-45a2-8ceb-3abaca70fcc7 · outbound

This paper cites Quantum similarity learning for anomaly detection.

Enhancing anomaly detection with topology-aware autoencoders Quantum similarity learning for anomaly detection

Reference 40

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Observation 003e89e4-12b4-466e-ada0-4c5a8c3a232f · outbound

This paper cites Searching for New Physics with Deep Autoencoders.

Enhancing anomaly detection with topology-aware autoencoders Searching for New Physics with Deep Autoencoders

Reference 41

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source=pdf_text observed=2026-08-07T19:19:24.349222Z digest=sha256:f1c2d07fb72c2f6e2588f67faa7b03c40bc2758fe248b7c231dc171bba15c904

Observation 51ef2df0-f8c3-4638-90ab-c9b7c76e335f · outbound

This paper cites QCD or What?.

Enhancing anomaly detection with topology-aware autoencoders QCD or What?

Reference 42

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source=pdf_text observed=2026-08-07T19:19:24.383451Z digest=sha256:9fd995cab8138541bd40390e79631019eaecf5a9c4f8cab91cda163bd7198a7e

Observation bf255bfe-d4f2-41de-9efe-81795ef58140 · outbound

This paper cites A robust anomaly finder based on autoencoders.

Enhancing anomaly detection with topology-aware autoencoders A robust anomaly finder based on autoencoders

Reference 43

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source=pdf_text observed=2026-08-07T19:19:24.414457Z digest=sha256:c43cd08aba1f13df1ce52cbd936081d493e4610f4f86c957177049cd98ee5ae1

Observation 600c7882-db7b-457d-a992-db0b0717fd07 · outbound

This paper cites Variational Autoencoders for New Physics Mining at the Large Hadron Collider.

Enhancing anomaly detection with topology-aware autoencoders Variational Autoencoders for New Physics Mining at the Large Hadron Collider

Reference 44

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source=pdf_text observed=2026-08-07T19:19:24.448069Z digest=sha256:c517482e92ff82bae58233d65afbf70e0b51b8f8940f4cf94e69c5ca68bb00ab

Observation acdf53cc-9345-45c6-a005-38da28249d00 · outbound

This paper cites Adversarially-trained autoencoders for robust unsupervised new physics searches.

Enhancing anomaly detection with topology-aware autoencoders Adversarially-trained autoencoders for robust unsupervised new physics searches

Reference 45

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source=pdf_text observed=2026-08-07T19:19:24.478391Z digest=sha256:a8a5dd36ea5a5c878ab0a110c441a5e9d3b3f56feaa96bc2370eeacc3ad70eca

Observation e7136392-0137-4d71-a4e9-b5b769522742 · outbound

This paper cites Combining outlier analysis algorithms to identify new physics at the LHC.

Enhancing anomaly detection with topology-aware autoencoders Combining outlier analysis algorithms to identify new physics at the LHC

Reference 46

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source=pdf_text observed=2026-08-07T19:19:24.484318Z digest=sha256:60525f9b61b99ec2037e4d1b63067b36cf5e45334d3756e773777332c05cc685

Observation 6c520b07-74a4-419b-affa-8b4ebb7c33cf · outbound

This paper cites Topological Obstructions to Autoencoding.

Enhancing anomaly detection with topology-aware autoencoders Topological Obstructions to Autoencoding

Reference 47

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source=pdf_text observed=2026-08-07T19:19:24.489612Z digest=sha256:67867138a897443e69cdc99706af82acb78bee399c6ff1946d12f88ad54424b2

Observation 945f8d14-8467-4a04-83ff-a341426495e9 · outbound

This paper cites Better Latent Spaces for Better Autoencoders.

Enhancing anomaly detection with topology-aware autoencoders Better Latent Spaces for Better Autoencoders

Reference 48

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source=pdf_text observed=2026-08-07T19:19:24.494291Z digest=sha256:d8de8340408a4b408549d918b29e86238c38f70cc8362b8cae1570214412cdca

Observation aea7daf5-c476-41e9-9344-70494a8ca1bc · outbound

This paper cites Autoencoders for unsupervised anomaly detection in high energy physics.

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

Reference 49

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source=pdf_text observed=2026-08-07T19:19:24.499761Z digest=sha256:2277f16828631fd110dd8288dd6ebec817dc38805dd512304109d77855b33e80

Observation f768af68-c621-4b04-8bbc-da0fa65ab60e · outbound

This paper cites Anomaly detection with Convolutional Graph Neural Networks.

Enhancing anomaly detection with topology-aware autoencoders Anomaly detection with Convolutional Graph Neural Networks

Reference 50

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source=pdf_text observed=2026-08-07T19:19:24.505670Z digest=sha256:275b63b4197d44c163253057292f45c3b7117428f4d587217184daa7e8c73e93

Observation 22fc5136-6a45-4211-874e-7bff21806a53 · outbound

This paper cites Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider.

Enhancing anomaly detection with topology-aware autoencoders Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider

Reference 51

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source=pdf_text observed=2026-08-07T19:19:24.510871Z digest=sha256:95a843afc5fce0a5bd9b4c3f7bb46e5dd0451147f652913423218a677502f027

Observation 4a8cb0fc-ea93-4e04-8585-0f0dddf14cdd · outbound

This paper cites Challenges for Unsupervised Anomaly Detection in Particle Physics.

Enhancing anomaly detection with topology-aware autoencoders Challenges for Unsupervised Anomaly Detection in Particle Physics

Reference 52

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source=pdf_text observed=2026-08-07T19:19:24.516293Z digest=sha256:a9f3d8e71a3f4de804f9a6dcde843f04106ffcaf798dd89f090d82eb821ee020

Observation 7562ce82-405a-4ade-8f99-c96d7cd55d44 · outbound

This paper cites Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance.

Enhancing anomaly detection with topology-aware autoencoders Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance

Reference 53

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source=pdf_text observed=2026-08-07T19:19:24.521741Z digest=sha256:9cce67a50065f1ec6faa4a4487b6e89ad43814097b1681caf085909c2f3a5643

Observation 5ebb1179-50b9-42e2-aa3b-e7b2e8152393 · outbound

This paper cites Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows.

Enhancing anomaly detection with topology-aware autoencoders Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows

Reference 54

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source=pdf_text observed=2026-08-07T19:19:24.526686Z digest=sha256:ca5e091e50780a9e3f2a12d8fe2b50c7b21bf816eca7dceef28bbf19a7bf5ee5

Observation 9947cfe7-47b8-44a9-8bd4-d634460a511f · outbound

This paper cites Autoencoders for Semivisible Jet Detection.

Enhancing anomaly detection with topology-aware autoencoders Autoencoders for Semivisible Jet Detection

Reference 55

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source=pdf_text observed=2026-08-07T19:19:24.532470Z digest=sha256:bb002446a2b454874a9961d87329f66f44d46fb499aba0bcb8f520d0531a7807

Observation 380da7d9-63a7-4010-90db-7d347e3d35d5 · outbound

This paper cites Anomaly detection in high-energy physics using a quantum autoencoder.

Enhancing anomaly detection with topology-aware autoencoders Anomaly detection in high-energy physics using a quantum autoencoder

Reference 56

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source=pdf_text observed=2026-08-07T19:19:24.551899Z digest=sha256:9d44aec273f5281843d68e25c5158067aa1ecc68810d65eb852012984ddcd3f7

Observation d70a860d-e920-4e47-b2a3-aa2166c7dac4 · outbound

This paper cites Quantum Anomaly Detection for Collider Physics.

Enhancing anomaly detection with topology-aware autoencoders Quantum Anomaly Detection for Collider Physics

Reference 57

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source=pdf_text observed=2026-08-07T19:19:24.557269Z digest=sha256:86911b2fcf089b69aaff167251a97dc2f37d42fd6322f18b49f3ebce44805468

Observation 408dd90e-807b-4c8d-b73b-accbda07d0d0 · outbound

This paper cites IRC-safe Graph Autoencoder for unsupervised anomaly detection.

Enhancing anomaly detection with topology-aware autoencoders IRC-safe Graph Autoencoder for unsupervised anomaly detection

Reference 58

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source=pdf_text observed=2026-08-07T19:19:24.562593Z digest=sha256:cfedd0aa4c92676383eb1d096f77d91c360e34743237bc0b69c9ad946360232e

Observation 9723e361-ade9-4078-ac26-2f1884aa9052 · outbound

This paper cites an unresolved cited work.

Enhancing anomaly detection with topology-aware autoencoders Unresolved cited work

Reference 59

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source=pdf_text observed=2026-08-07T19:19:24.570231Z digest=sha256:41187afe0e8c1506002581d2d376f865f9c99b9c4b128b179daf63f6db67595f

Observation 1d3bdb84-d381-43d5-aee8-886c49139d0b · outbound

This paper cites LLPNet: Graph Autoencoder for Triggering Light Long-Lived Particles at HL-LHC.

Enhancing anomaly detection with topology-aware autoencoders LLPNet: Graph Autoencoder for Triggering Light Long-Lived Particles at HL-LHC

Reference 60

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source=pdf_text observed=2026-08-07T19:19:24.576493Z digest=sha256:f90afb22e17d76b75c6f1d0f7709acc1290dd233dab8c132f493aef427b08974

Observation 8d005515-04c5-4a64-8d19-3fac1bac2f8d · outbound

This paper cites ATLAS flavour-tagging algorithms for the LHC Run 2 $pp$ collision dataset.

Enhancing anomaly detection with topology-aware autoencoders ATLAS flavour-tagging algorithms for the LHC Run 2 $pp$ collision dataset

Reference 61

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source=pdf_text observed=2026-08-07T19:19:24.581955Z digest=sha256:8e4186404562aa0af849532a63ae65c204007251a051f94c430856229d825748

Observation dea1e066-ff4e-4208-a3cb-5bf7bd8b0523 · outbound

This paper cites Rep., CERN, Geneva (2016), URLhttps://cds.cern.ch/record/2205149.

Enhancing anomaly detection with topology-aware autoencoders Rep., CERN, Geneva (2016), URLhttps://cds.cern.ch/record/2205149

Reference 62

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source=pdf_text observed=2026-08-07T19:19:24.588573Z digest=sha256:62f2176d5ebcdbd99d07fad9b6b45c61b3b3403a55b34fb4815ff3f76b726785

Observation 29040a66-a933-4dfa-b622-be71551ff994 · outbound

This paper cites an unresolved cited work.

Enhancing anomaly detection with topology-aware autoencoders Unresolved cited work

Reference 63

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Observation 4cc19550-a356-4fce-a7d4-216462fc2a3d · outbound

This paper cites Whitney, Annals of Mathematics37, 645 (1936), ISSN 0003486X, 19398980, URLhttp://www.jstor.org/stable/ 1968482.

Enhancing anomaly detection with topology-aware autoencoders Whitney, Annals of Mathematics37, 645 (1936), ISSN 0003486X, 19398980, URLhttp://www.jstor.org/stable/ 1968482

Reference 64

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source=pdf_text observed=2026-08-07T19:19:24.600887Z digest=sha256:39b28c901705d773228a084af3fa700ef721356d67361275b2368778f1b0a3c5

Observation da5d205f-2dc8-4419-9d94-e637cf2b287b · outbound

This paper cites Whitney, Annals of Mathematics45, 220 (1944), ISSN 0003486X, 19398980, URLhttp://www.jstor.org/stable/ 1969265.

Enhancing anomaly detection with topology-aware autoencoders Whitney, Annals of Mathematics45, 220 (1944), ISSN 0003486X, 19398980, URLhttp://www.jstor.org/stable/ 1969265

Reference 65

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source=pdf_text observed=2026-08-07T19:19:24.607080Z digest=sha256:faa0f9bbbd3658048a553ec64a8759d144053e906d93f4c08a92f3854d2f1962

Observation c7e11e50-4a7c-433b-a07e-f7e833f03429 · outbound

This paper cites The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations.

Enhancing anomaly detection with topology-aware autoencoders The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations

Reference 66

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source=pdf_text observed=2026-08-07T19:19:24.612783Z digest=sha256:518715fa9ee4efc217e7714901fbd23f988a19aafe08c01ced88c87c10ec3e26

Observation 6869f41d-ed69-41db-ac75-4472b55da65a · outbound

This paper cites A comprehensive guide to the physics and usage of PYTHIA 8.3.

Enhancing anomaly detection with topology-aware autoencoders A comprehensive guide to the physics and usage of PYTHIA 8.3

Reference 67

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source=pdf_text observed=2026-08-07T19:19:24.618013Z digest=sha256:df9e51a483bd9eba8d836adb14b99459e75a1f2e4ee6f0c2d260f23b6553cfb3

Observation 2e723911-88aa-413b-b21a-aa390955829a · outbound

This paper cites The SMEFTsim package, theory and tools.

Enhancing anomaly detection with topology-aware autoencoders The SMEFTsim package, theory and tools

Reference 68

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source=pdf_text observed=2026-08-07T19:19:24.623572Z digest=sha256:92d0f7ec0792241a1c9ee81d55efd086b39e156274050a3f3089feb412dc5f1a

Observation a31cfe17-e548-48a1-b194-74d24bbff3c4 · outbound

This paper cites SMEFTsim 3.0 -- a practical guide.

Enhancing anomaly detection with topology-aware autoencoders SMEFTsim 3.0 -- a practical guide

Reference 69

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source=pdf_text observed=2026-08-07T19:19:24.629588Z digest=sha256:2bb5482a457703a536fb96d818de056da9c030f3b4cb917e7503ca588fc7b21e

Observation 11f99575-f252-4d23-aa0b-640255f8e6ff · outbound

This paper cites The anti-k_t jet clustering algorithm.

Enhancing anomaly detection with topology-aware autoencoders The anti-k_t jet clustering algorithm

Reference 70

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source=pdf_text observed=2026-08-07T19:19:24.635614Z digest=sha256:33240915f6e4444f066e2aa44a740bddd21d088bb33f174c471d09d259d75308

Observation eef8fc54-e9ab-49d8-86f0-4d52f79dc0aa · outbound

This paper cites FastJet user manual.

Enhancing anomaly detection with topology-aware autoencoders FastJet user manual

Reference 71

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source=pdf_text observed=2026-08-07T19:19:24.640655Z digest=sha256:07ee6bfda15bf129ac5655c81516cbc57a902b767d5e79dc2c8029f4be0fbfc0

Observation 5e956435-cee7-4cad-8111-ae8d102c2d4f · outbound

This paper cites Pedregosa, G.

Enhancing anomaly detection with topology-aware autoencoders Pedregosa, G

Reference 72

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source=pdf_text observed=2026-08-07T19:19:24.645829Z digest=sha256:f0dd23894f164c03518ffb1212a2a6cf8fc15de75f3c966e8bc6b748c5acd793

Observation 8b069c80-977c-4969-9706-3b14b5b781f5 · outbound

This paper cites Paszke, S.

Enhancing anomaly detection with topology-aware autoencoders Paszke, S

Reference 73

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source=pdf_text observed=2026-08-07T19:19:24.650572Z digest=sha256:bc7789f61e05741fecfc11092562b398236b92f33eca4fbc30689dfc68178308

Observation d3d6ea0a-fc5c-47e5-aefe-82a9c6dba453 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Enhancing anomaly detection with topology-aware autoencoders Adam: A Method for Stochastic Optimization

Reference 74

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source=pdf_text observed=2026-08-07T19:19:24.655485Z digest=sha256:4108614e66b7aa627faacc31e2b4c8c1f72f1486c4251697e9230fa93ea3792a

Pith citing papers

Observation 23347f6c-9390-4947-8137-d54615893206 · inbound

Graph theory inspired anomaly detection at the LHC cites this paper.

Graph theory inspired anomaly detection at the LHC Enhancing anomaly detection with topology-aware autoencoders

Reference 98

Resolution
verified exact
local_arxiv, observed 2026-08-15T18:31:43.733412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T18:31:43.153781Z digest=sha256:a12a1a21b6fc1f7947de53ec7b2213c06c77294024551c2c1a928729ab5bfbab