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Paper Citation Record · LEDGER

Look everywhere effects in anomaly detection

As of 9 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 2 inbound Pith citation observations for arXiv:2512.13787.

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

pith.paper-citation-record.v1
2512.13787 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T16:25:13.874890Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:04:30.343017Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T14:06:04.024762Z

Reference resolution

50 of 50 outbound references displayed

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  • verified fuzzy0
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  • malformed identifier0
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External citation measurements

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Outbound references

Observation 37ad8e09-cd16-4c75-8496-80f639738d45 · outbound

This paper cites an unresolved cited work.

Look everywhere effects in anomaly detection Unresolved cited work

Reference 1

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Observation 6e05eaa6-fc21-4688-b7bc-f96a0c074fa6 · outbound

This paper cites We use default hyperparameters including early stop- ping with a patience of ten epochs, where we revert back to the best model.

Look everywhere effects in anomaly detection We use default hyperparameters including early stop- ping with a patience of ten epochs, where we revert back to the best model

Reference 2

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Observation 052bc51b-5328-4af4-a23c-a4276aeece8a · outbound

This paper cites For both binned and ML methods, we sampled 25,000 events in the SR to serve as the data.

Look everywhere effects in anomaly detection For both binned and ML methods, we sampled 25,000 events in the SR to serve as the data

Reference 3

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Observation 87517440-f2a8-4b60-811a-6595bf24c59e · outbound

This paper cites This data set contains a QCD dijet background andW ′ resonance signal atm W ′ = 3.5 TeV, which decays intoX(→qq) and Y(→qq) with masses mX = 100 GeV andm Y = 500 GeV, respectively.

Look everywhere effects in anomaly detection This data set contains a QCD dijet background andW ′ resonance signal atm W ′ = 3.5 TeV, which decays intoX(→qq) and Y(→qq) with masses mX = 100 GeV andm Y = 500 GeV, respectively

Reference 4

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Observation 697fb094-c984-404b-b519-f1ff9769af63 · outbound

This paper cites training.

Look everywhere effects in anomaly detection training

Reference 5

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Observation 05fe114c-b3e5-442d-8326-ad1019ae9227 · outbound

This paper cites When optimizing an analy- sis, the ideal bin selection or weight configuration in a NN would rely exclusively on the distributions underlying the data.

Look everywhere effects in anomaly detection When optimizing an analy- sis, the ideal bin selection or weight configuration in a NN would rely exclusively on the distributions underlying the data

Reference 6

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Observation a65bf1f5-5f44-46b9-8a20-6d747a9fa71d · outbound

This paper cites an unresolved cited work.

Look everywhere effects in anomaly detection Unresolved cited work

Reference 7

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Observation f4e94653-914d-40c7-8d7a-e71de5c7f03e · outbound

This paper cites Taken in isolation, bothN ′ BT and N ′ SR follow a Poisson distribution with the common ex- pectation valueλ.

Look everywhere effects in anomaly detection Taken in isolation, bothN ′ BT and N ′ SR follow a Poisson distribution with the common ex- pectation valueλ

Reference 8

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Observation 041ac3f4-ff9f-4b9e-9ba4-93e447182a90 · outbound

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

Look everywhere effects in anomaly detection The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics

Reference 9

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Observation 3b46685a-c591-400b-abc3-1e9d032f6163 · outbound

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

Look everywhere effects in anomaly detection The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

Reference 10

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Observation 62a31620-6dad-4180-94d6-f39371373444 · outbound

This paper cites Machine Learning in the Search for New Fundamental Physics.

Look everywhere effects in anomaly detection Machine Learning in the Search for New Fundamental Physics

Reference 11

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Observation 18064867-2f9c-4a74-a00f-abcb32a3a09c · outbound

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

Look everywhere effects in anomaly detection Machine Learning for Anomaly Detection in Particle Physics

Reference 12

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Observation f9b5f8ba-e06d-4a94-b2cc-394534cbb6ec · outbound

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

Look everywhere effects in anomaly detection Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector

Reference 13

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Observation 84fa43ac-2dc3-4ee6-9673-3b23fb6f65dd · outbound

This paper cites Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at $\sqrt{s}$ = 13 TeV.

Look everywhere effects in anomaly detection Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at $\sqrt{s}$ = 13 TeV

Reference 14

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Observation 8c7a96a1-0e4f-4bbb-b310-c8dca0a771af · outbound

This paper cites Aadet al.(ATLAS), Phys.

Look everywhere effects in anomaly detection Aadet al.(ATLAS), Phys

Reference 15

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Observation 0e279fd0-a13e-4c17-8501-3d1a389a2e6b · outbound

This paper cites Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data.

Look everywhere effects in anomaly detection Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data

Reference 16

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Observation 00b40f7d-2fe2-4a81-b418-a284d37a9869 · outbound

This paper cites Trial factors for the look elsewhere effect in high energy physics.

Look everywhere effects in anomaly detection Trial factors for the look elsewhere effect in high energy physics

Reference 17

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Observation ff3e9fd4-a13f-4199-a2e4-ee1ef4dbb0ab · outbound

This paper cites MUSiC: a model-unspecific search for new physics in proton-proton collisions at $\sqrt{s} = $ 13 TeV.

Look everywhere effects in anomaly detection MUSiC: a model-unspecific search for new physics in proton-proton collisions at $\sqrt{s} = $ 13 TeV

Reference 18

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Observation 571b40e1-3e30-4211-ad35-05682dac7287 · outbound

This paper cites an unresolved cited work.

Look everywhere effects in anomaly detection Unresolved cited work

Reference 19

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Observation c28dedc6-b4f2-4d0e-be15-ba6bfd82bade · outbound

This paper cites Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection.

Look everywhere effects in anomaly detection Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection

Reference 20

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Observation a9afbf7c-67bb-4440-924d-2d327d3e83cf · outbound

This paper cites Anomaly Detection in Presence of Irrelevant Features.

Look everywhere effects in anomaly detection Anomaly Detection in Presence of Irrelevant Features

Reference 21

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Observation 0cd4c621-47dc-4d4e-91e8-c44eef4cddd8 · outbound

This paper cites Mikuni and B.

Look everywhere effects in anomaly detection Mikuni and B

Reference 22

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Observation 830a9ee8-a2c7-4c69-9a4e-0e9a340e02b5 · outbound

This paper cites Bhimji, C.

Look everywhere effects in anomaly detection Bhimji, C

Reference 23

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Observation dac92d37-0bbc-4ea6-89fd-9dc564a75280 · outbound

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

Look everywhere effects in anomaly detection Incorporating Physical Priors into Weakly-Supervised Anomaly Detection

Reference 24

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Observation a548a97d-058d-4c55-b2e4-827017ccfb61 · outbound

This paper cites Generator Based Inference (GBI).

Look everywhere effects in anomaly detection Generator Based Inference (GBI)

Reference 25

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Observation e7d4a90f-2887-4926-b3e8-0568c22cf79f · outbound

This paper cites Multiple testing for signal-agnostic searches of new physics with machine learning.

Look everywhere effects in anomaly detection Multiple testing for signal-agnostic searches of new physics with machine learning

Reference 26

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Observation bcd58458-90c9-42f9-9d89-73c1d7d97b5e · outbound

This paper cites The Interplay of Machine Learning--based Resonant Anomaly Detection Methods.

Look everywhere effects in anomaly detection The Interplay of Machine Learning--based Resonant Anomaly Detection Methods

Reference 27

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Observation 93708354-1d28-4503-bbf9-4b9326a359b2 · outbound

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

Look everywhere effects in anomaly detection Anomaly Detection for Resonant New Physics with Machine Learning

Reference 28

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Observation d5fe86f6-1f2c-42f7-b275-bd89ff4dc10b · outbound

This paper cites Extending the Bump Hunt with Machine Learning.

Look everywhere effects in anomaly detection Extending the Bump Hunt with Machine Learning

Reference 29

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Observation d3d25006-4a7d-48ba-9918-232f8f995c92 · outbound

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

Look everywhere effects in anomaly detection Classifying Anomalies THrough Outer Density Estimation (CATHODE)

Reference 30

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Observation 7f08e44b-3d24-4a41-b528-ef3b901fc0ba · outbound

This paper cites Classification without labels: Learning from mixed samples in high energy physics.

Look everywhere effects in anomaly detection Classification without labels: Learning from mixed samples in high energy physics

Reference 31

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Observation 7bcb4588-9daa-455d-a8ba-fe726a839713 · outbound

This paper cites Null Hypothesis Test for Anomaly Detection.

Look everywhere effects in anomaly detection Null Hypothesis Test for Anomaly Detection

Reference 32

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Observation ebf87839-e7a1-4c49-97b7-9b1fc9419d55 · outbound

This paper cites Learning New Physics from a Machine.

Look everywhere effects in anomaly detection Learning New Physics from a Machine

Reference 33

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Observation 59870df2-6d98-4d67-b565-fb462e05826a · outbound

This paper cites Learning Multivariate New Physics.

Look everywhere effects in anomaly detection Learning Multivariate New Physics

Reference 34

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Observation 0a2ee206-2003-4683-a138-bdf4000f3e76 · outbound

This paper cites Neyman and E.

Look everywhere effects in anomaly detection Neyman and E

Reference 35

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Observation a3c7a3f7-a0f6-4997-a72f-77c67e01056f · outbound

This paper cites TensorFlow: Large-scale machine learn- ing on heterogeneous systems,.

Look everywhere effects in anomaly detection TensorFlow: Large-scale machine learn- ing on heterogeneous systems,

Reference 36

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Observation 15d823a3-7c72-479e-a346-7889d1538ca8 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Look everywhere effects in anomaly detection Adam: A Method for Stochastic Optimization

Reference 37

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Observation e88cb211-4cfa-4e80-bd82-8a31689e7a45 · outbound

This paper cites Pedregosaet al., Journal of Machine Learning Re- search12, 2825 (2011).

Look everywhere effects in anomaly detection Pedregosaet al., Journal of Machine Learning Re- search12, 2825 (2011)

Reference 38

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Observation 73913f3c-e7eb-4b40-bd12-7e54dfeca2ef · outbound

This paper cites R&d dataset for lhc olympics 2020 anomaly detection challenge,.

Look everywhere effects in anomaly detection R&d dataset for lhc olympics 2020 anomaly detection challenge,

Reference 39

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Observation 514bcb89-cb58-4cf7-adc7-d7418c83209a · outbound

This paper cites PYTHIA 6.4 Physics and Manual.

Look everywhere effects in anomaly detection PYTHIA 6.4 Physics and Manual

Reference 40

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source=pdf_text observed=2026-08-03T16:25:12.558890Z digest=sha256:10ff9173125472ddc60eff8f00492da909824c1ce80582453d41470e41bf402e

Observation 0bab577a-16e3-44d8-ad6e-d1333bab752d · outbound

This paper cites A Brief Introduction to PYTHIA 8.1.

Look everywhere effects in anomaly detection A Brief Introduction to PYTHIA 8.1

Reference 41

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source=pdf_text observed=2026-08-03T16:25:12.658845Z digest=sha256:dac72259f9cc64d15e58e3f071dfab8bbe6517f61f7201260430af2b3cf51ead

Observation 6c062b93-5924-45d7-8370-e872618cae2a · outbound

This paper cites DELPHES 3, A modular framework for fast simulation of a generic collider experiment.

Look everywhere effects in anomaly detection DELPHES 3, A modular framework for fast simulation of a generic collider experiment

Reference 42

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source=pdf_text observed=2026-08-03T16:25:12.818849Z digest=sha256:661c397bd22604491ab8445f572db993520a435f56385ef09ad47007006a4c3c

Observation 5f17ab5e-b57a-47ff-84bf-492b7eb967f1 · outbound

This paper cites Dispelling the N^3 myth for the Kt jet-finder.

Look everywhere effects in anomaly detection Dispelling the N^3 myth for the Kt jet-finder

Reference 43

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no resolver link, observed 2026-08-03T16:25:12.988990Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T16:25:12.988990Z digest=sha256:002c7fd4b3efa099d73215ec0e242cc6135d1e1807ad67195394ba33e51b5319

Observation b8093f9c-e7a5-4c3c-99d9-5133d3044b05 · outbound

This paper cites FastJet user manual.

Look everywhere effects in anomaly detection FastJet user manual

Reference 44

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no resolver link, observed 2026-08-03T16:25:13.070931Z

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source=pdf_text observed=2026-08-03T16:25:13.070931Z digest=sha256:2f6955d043702e5cbc4ed2af05fec3231f0fc95025fd3950e2e0340925812f45

Observation 7488614e-2d88-446f-9b39-59f6fe3ff741 · outbound

This paper cites Identifying Boosted Objects with N-subjettiness.

Look everywhere effects in anomaly detection Identifying Boosted Objects with N-subjettiness

Reference 45

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source=pdf_text observed=2026-08-03T16:25:13.231774Z digest=sha256:782bf66e048037dec1444f74d76d9ee01a33156de780a1bd966eb33e77812207

Observation d189f1d2-1c8d-4c3f-8570-c2635561829a · outbound

This paper cites Maximizing Boosted Top Identification by Minimizing N-subjettiness.

Look everywhere effects in anomaly detection Maximizing Boosted Top Identification by Minimizing N-subjettiness

Reference 46

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source=pdf_text observed=2026-08-03T16:25:13.398022Z digest=sha256:c7387ea707795edf8f09075b45188f0750c4a8b277d74f46b542bdaae5b6b906

Observation 90574c45-26e5-45fc-811f-d5b28c35b6b1 · outbound

This paper cites Additional qcd background events for lhco2020 r&d (signal region only),.

Look everywhere effects in anomaly detection Additional qcd background events for lhco2020 r&d (signal region only),

Reference 47

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source=pdf_text observed=2026-08-03T16:25:13.507711Z digest=sha256:d94c207b9761897f2c3fbe23cebc990c349f7cae76dedda7bcc7df60df9b7500

Observation 93aee5a5-77bd-40e5-a9b0-c712dd475a59 · outbound

This paper cites an unresolved cited work.

Look everywhere effects in anomaly detection Unresolved cited work

Reference 48

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Observation 3cfd61e2-4c04-4ee0-81ae-8a0a6db04427 · outbound

This paper cites in prepara- tion,.

Look everywhere effects in anomaly detection in prepara- tion,

Reference 49

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source=pdf_text observed=2026-08-03T16:25:13.770324Z digest=sha256:47d9af6a70733be99a0dca09550a8fddd97711b6ba7b170707e74f217038b4e0

Observation b9734f01-182f-4808-a2a1-6bf8b0509d37 · outbound

This paper cites Look everywhere effects in anomaly de- tection,.

Look everywhere effects in anomaly detection Look everywhere effects in anomaly de- tection,

Reference 50

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source=pdf_text observed=2026-08-03T16:25:13.874890Z digest=sha256:dbe8ad7efdf5b4b4bb44723fe448c3d67fc50648a2eb4ec31c48cfbe371b6b81

Pith citing papers

Observation eee7b75e-cbd0-43f6-afdd-48d4be2356eb · inbound

Kitchen Sink Anomaly Detection cites this paper.

Kitchen Sink Anomaly Detection Look everywhere effects in anomaly detection

Reference 25

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arxiv_id, observed 2026-06-23T04:14:07.292683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-09T23:30:30.344180Z digest=sha256:6a387bab52d55181e478dd4046ec82c5aecb3664636dc3cc2018957e289da5ff

Observation 1245678f-4079-4769-b4b1-709c6b92e319 · inbound

Machine Learning is Good for Physics - and Vice Versa cites this paper.

Machine Learning is Good for Physics - and Vice Versa Look everywhere effects in anomaly detection

Reference 20

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source=pdf_text observed=2026-08-07T23:04:30.343017Z digest=sha256:226ca141e25946361d77b2ff94cceae9caa3e1fa605c759393c82b64b4235386