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Paper Citation Record · LEDGER

Enhancing anomaly detection with topology-aware autoencoders

As of 8 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 0 inbound Pith citation observations 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

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

measured 74 of 74 standing notices

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

74 of 74 outbound references displayed

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  • verified fuzzy4
  • unresolved51
  • parse uncertain0
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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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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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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=sha256:9f0c9abc2b060065328942c2dffa122c1ab4ce8ab2c5f2e36f68679a8afcc15b

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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source=pdf_text observed=2026-08-07T19:19:24.241393Z digest=sha256:97869553528680b56b5349f154512cb74c53e4f5287b51ae47566dd4773051a6

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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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

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

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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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source=pdf_text observed=2026-08-07T19:19:24.321077Z digest=sha256:f9837d499ba3ce795ce1afa89dc9e6d1433e336233970d9b8b3580a2ef2e0b7a

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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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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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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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:f8297d61c1d8220ce2c9eb3aecfa83f62a4160266f7e2767f50d0fa9416242eb

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=sha256:6d06557df21aef1874b26b4f0a580131b70733e5f8f5d1977b7c8f5024451fca

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=sha256:721debf2ad165a4a77ec3071351443da846aa9647ba280ad6f7d8c9ed84a1b72

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=sha256:2926d3b794c39416198a1284b0ec08a143c25bbaefc548fd060710b132ffd581

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=sha256:6dc27fea76e2d30fac9b8e171ef6aa670bf9f89f29b622254a67f300fd6b6206

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=sha256:40bb3baebbddc2cc28bf51af46cf07c8cc6ffccaf503364495eb4eb720b3c963

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:db5368580155e5549d81271a7e3836c532703ad57e885d1cf9c0baa243a5e4c5

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=sha256:26fb2cb2fb9cf75b96c0da97ee1ee3f8af6d534cf015317deb3fcf3a5d692fe9

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=sha256:3cfb8260d929fac751c6e3d2598c1f0a32ec30b16d95cb9cd04d8faf8a3d424d

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=sha256:79ca12ecdf354e886ea7ad564aa6268ce040d6bdf99db33266da303faf9b9f13

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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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=sha256:6d678bd49290057fa5616e84e7739d949da3cb826dfda091809717474ea720b1

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:a64c35a2afab9f65a45f48c070d7bbee09b984730e27fdab067552a7686e7d71

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=sha256:5de2dfb7af7163b1d517247f5c5ed75514c449fcacd93ebe6bea8cbb40953792

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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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=sha256:0cd64d0a22967dc0b618ce93e8a985b2e368bf1b831608e4126f05693d13421b

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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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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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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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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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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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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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=sha256:6168c5e3c2d33dcef25de4b2286f4cf5479ef747ce613b493e8e3cdb782990c6

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=sha256:5a27df03fe59d76957da236286db66d1ed52a1f002d0c3e2d0e227a475112539

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=sha256:17fa81cc5579b62258ab7ca23721030ffd4fab19eb551d490f22f4398068b93a

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=sha256:8ec2d7d7ff1b95d22e796245520be8e26a193f01079a19ea981e9434ee4345f5

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=sha256:0deaf4492f202d37b94d2f0b4033243002fef8b41c7787df9a0652ead55beb67

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:cd374673b4055047ca06202baec7a43bea1f2af5ad64f61b1985055d6e421ba8

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=sha256:7b3be30a9ac657b73d5f385dd828fcf007607f1054954b03181a8e7fe5a9ddc1

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

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Pith citing papers

No inbound Pith citation observations are available.