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Paper Citation Record · LEDGER

Analysis-ready Generative Unfolding

As of 23 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 2 inbound Pith citation observations for arXiv:2509.02708.

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

pith.paper-citation-record.v1
2509.02708 v1

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:41:37.018709Z

measured 68 of 68 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T23:36:55.617103Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T15:25:47.359001Z

Reference resolution

66 of 66 outbound references displayed

  • verified exact2
  • verified fuzzy1
  • unresolved63
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 422dfd15-cb87-4a5e-8894-27d0662130f3 · outbound

This paper cites The frontier of simulation-based inference.

Analysis-ready Generative Unfolding The frontier of simulation-based inference

Reference 1

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Source-reported events for the cited work

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Observation e8c2cd4d-bac7-48b0-8940-1450876c3755 · outbound

This paper cites Presenting Unbinned Differential Cross Section Results.

Analysis-ready Generative Unfolding Presenting Unbinned Differential Cross Section Results

Reference 2

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source=pdf_text observed=2026-08-15T16:41:36.707623Z digest=sha256:e8c862fd369fa0c60cf6347da5998a79c74d27ba37e9dc95c4920a5a989779c6

Observation 10784fe9-56d6-43b0-8dc6-580de0ea9313 · outbound

This paper cites The Landscape of Unfolding with Machine Learning.

Analysis-ready Generative Unfolding The Landscape of Unfolding with Machine Learning

Reference 3

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source=pdf_text observed=2026-08-15T16:41:36.712623Z digest=sha256:6b17e6e5606eddc15d268220acc40994b649f716ff14ffea28603e95fec5d0f5

Observation c103547e-0be4-47e7-ac3d-2b416694488e · outbound

This paper cites Canelli et al.,A Practical Guide to Unbinned Unfolding, 2507.09582.

Analysis-ready Generative Unfolding Canelli et al.,A Practical Guide to Unbinned Unfolding, 2507.09582

Reference 4

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source=pdf_text observed=2026-08-15T16:41:36.717608Z digest=sha256:3baa1ce2161d05d952f80d6cbe305f80f81d348d01248a05ac536535e6951289

Observation 6f3ee5ed-15da-4fd4-8090-3a8013deab43 · outbound

This paper cites OmniFold: A Method to Simultaneously Unfold All Observables.

Analysis-ready Generative Unfolding OmniFold: A Method to Simultaneously Unfold All Observables

Reference 5

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source=pdf_text observed=2026-08-15T16:41:36.722607Z digest=sha256:474589b8c4367b980a48a9d11065fdb9cdcc26513cddbb623cd0a7b89ffff83c

Observation a89b6519-fb33-4386-b924-4e2e6ba4efec · outbound

This paper cites Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution.

Analysis-ready Generative Unfolding Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution

Reference 6

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.728247Z digest=sha256:3bb9581190440c40fd9586ede88e1037c32e5ef4d3193bde4206c02c5807db66

Observation 1dd4c517-39a7-4aa1-8a56-8874cd765efe · outbound

This paper cites A simultaneous unbinned differential cross section measurement of twenty-four $Z$+jets kinematic observables with the ATLAS detector.

Analysis-ready Generative Unfolding A simultaneous unbinned differential cross section measurement of twenty-four $Z$+jets kinematic observables with the ATLAS detector

Reference 7

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source=pdf_text observed=2026-08-15T16:41:36.733508Z digest=sha256:c626ebcdade171a0c179666eb3abf2a2ceb6dccf342e2d473f177352b96679d7

Observation a9879577-ad3d-4650-87e8-4cea8d1f9fae · outbound

This paper cites Measurement of jet track functions in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector.

Analysis-ready Generative Unfolding Measurement of jet track functions in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

Reference 8

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source=pdf_text observed=2026-08-15T16:41:36.738999Z digest=sha256:8af4b896093a26cd6b4591a42324ff34ebafa753d92a908c5c3ce29dcac4a129

Observation e6c614c4-490b-4b01-82ae-7650e2a20651 · outbound

This paper cites Disentangling Quarks and Gluons with CMS Open Data.

Analysis-ready Generative Unfolding Disentangling Quarks and Gluons with CMS Open Data

Reference 9

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source=pdf_text observed=2026-08-15T16:41:36.744180Z digest=sha256:353feeffbe1403da053f9e97a2f8e83785423da9cdff77f1dd232a8fff6e8b90

Observation 861caae6-c1a3-4ad2-b2cb-c0dcaa979b14 · outbound

This paper cites an unresolved cited work.

Analysis-ready Generative Unfolding Unresolved cited work

Reference 10

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Observation 67c971ab-adba-4ad4-9132-d293078845fb · outbound

This paper cites Multidifferential study of identified charged hadron distributions in $Z$-tagged jets in proton-proton collisions at $\sqrt{s}=$13 TeV.

Analysis-ready Generative Unfolding Multidifferential study of identified charged hadron distributions in $Z$-tagged jets in proton-proton collisions at $\sqrt{s}=$13 TeV

Reference 11

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source=pdf_text observed=2026-08-15T16:41:36.756384Z digest=sha256:14be1e9d9bc426561fb7d9fa34e36ae9e399c94d3afac2b311297d3808cf4575

Observation bfd11698-e253-4a49-a024-d2b83fd04c0e · outbound

This paper cites an unresolved cited work.

Analysis-ready Generative Unfolding Unresolved cited work

Reference 12

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source=pdf_text observed=2026-08-15T16:41:36.761407Z digest=sha256:4f3b9cef6497265a0ded55ae30223b6ff34cb727b6af8bf07473c43f4e43cbb9

Observation 804e9882-d61a-4f50-95f3-41e88a164e34 · outbound

This paper cites an unresolved cited work.

Analysis-ready Generative Unfolding Unresolved cited work

Reference 13

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Observation a5b66b1c-bddf-4612-944b-529a7536cb37 · outbound

This paper cites an unresolved cited work.

Analysis-ready Generative Unfolding Unresolved cited work

Reference 14

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source=pdf_text observed=2026-08-15T16:41:36.771194Z digest=sha256:515917566ff60d4617d29619eca9fa4178710043a57de5d8a705d72ebec2d564

Observation bcb2927c-d313-4e58-9183-d1c671d80ae1 · outbound

This paper cites Towards Unfolding All Particles in HighQ2 DIS Events.

Analysis-ready Generative Unfolding Towards Unfolding All Particles in HighQ2 DIS Events

Reference 15

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 01f7be8d-ed1f-49b1-8370-59f2dca085f7 · outbound

This paper cites Measurement of CollinearDrop jet mass and its correlation with SoftDrop groomed jet substructure observables in $\sqrt{s}=200$ GeV $pp$ collisions by STAR.

Analysis-ready Generative Unfolding Measurement of CollinearDrop jet mass and its correlation with SoftDrop groomed jet substructure observables in $\sqrt{s}=200$ GeV $pp$ collisions by STAR

Reference 16

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source=pdf_text observed=2026-08-15T16:41:36.780307Z digest=sha256:01f28d49daa1e8a645b467352ef3e3ab722d5ebb686b54be127d30e9efa3b521

Observation c5daf417-2bc5-438b-946a-15924604d47d · outbound

This paper cites Generalized angularities measurements from STAR at $\sqrt{s_{\rm NN}} = $ 200 GeV.

Analysis-ready Generative Unfolding Generalized angularities measurements from STAR at $\sqrt{s_{\rm NN}} = $ 200 GeV

Reference 17

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Observation cd6b6750-8824-4c80-9816-9a85b337a2be · outbound

This paper cites Machine Learning-Assisted Unfolding for Neutrino Cross-section Measurements with the OmniFold Technique.

Analysis-ready Generative Unfolding Machine Learning-Assisted Unfolding for Neutrino Cross-section Measurements with the OmniFold Technique

Reference 18

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Observation 44939c5c-1fea-4791-bae5-4e1f1e69ceed · outbound

This paper cites Analysis note: measurement of thrust in $e^{+}e^{-}$ collisions at $\sqrt{s}$ = 91 GeV with archived ALEPH data.

Analysis-ready Generative Unfolding Analysis note: measurement of thrust in $e^{+}e^{-}$ collisions at $\sqrt{s}$ = 91 GeV with archived ALEPH data

Reference 19

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Observation 0c9a3d30-915d-4728-ade1-5cb7d2bece6a · outbound

This paper cites Unfolding with Generative Adversarial Networks.

Analysis-ready Generative Unfolding Unfolding with Generative Adversarial Networks

Reference 20

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Observation d71494b5-649c-4b35-ba26-f389d0233f26 · outbound

This paper cites How to GAN away Detector Effects.

Analysis-ready Generative Unfolding How to GAN away Detector Effects

Reference 21

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Observation db061866-ce4f-4876-9b91-89948cbaf9e7 · outbound

This paper cites Invertible Networks or Partons to Detector and Back Again.

Analysis-ready Generative Unfolding Invertible Networks or Partons to Detector and Back Again

Reference 22

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Observation b31d9188-ebba-42db-8e22-66765992dbbe · outbound

This paper cites Learning to Simulate High Energy Particle Collisions from Unlabeled Data.

Analysis-ready Generative Unfolding Learning to Simulate High Energy Particle Collisions from Unlabeled Data

Reference 23

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Observation 1a078651-c5a1-4515-86f4-1af589e2fc5c · outbound

This paper cites An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training.

Analysis-ready Generative Unfolding An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training

Reference 24

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Observation 0be1c044-e7fc-43b3-bd20-9a8876555c51 · outbound

This paper cites Returning CP-Observables to The Frames They Belong.

Analysis-ready Generative Unfolding Returning CP-Observables to The Frames They Belong

Reference 25

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source=pdf_text observed=2026-08-15T16:41:36.825617Z digest=sha256:71a13d1913556044fba5a8f82b3b3e9d6971c8ca999670e437933a5e1f1c9fbe

Observation 321693f9-9459-407a-8b93-aa1178fa7248 · outbound

This paper cites End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics.

Analysis-ready Generative Unfolding End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics

Reference 26

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Observation d71a4094-24dc-498e-8021-16d3cdf55cf1 · outbound

This paper cites Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion.

Analysis-ready Generative Unfolding Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion

Reference 27

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Observation 7ca3746f-803a-4528-a682-384d1074fc43 · outbound

This paper cites Towards Universal Unfolding of Detector Effects in High-Energy Physics using Denoising Diffusion Probabilistic Models.

Analysis-ready Generative Unfolding Towards Universal Unfolding of Detector Effects in High-Energy Physics using Denoising Diffusion Probabilistic Models

Reference 28

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verified exact
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Source-reported events for the cited work

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Observation afd27b27-ee21-42d2-8ce0-9c40811979e8 · outbound

This paper cites How to Unfold Top Decays.

Analysis-ready Generative Unfolding How to Unfold Top Decays

Reference 29

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Source-reported events for the cited work

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Observation ae6271bb-709f-45c1-aef8-114d45cefb3f · outbound

This paper cites Diefenbacher, G.-H.

Analysis-ready Generative Unfolding Diefenbacher, G.-H

Reference 30

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Observation 32d0d641-be6a-4149-96ff-2763c3127e93 · outbound

This paper cites Kicking it Off(-shell) with Direct Diffusion.

Analysis-ready Generative Unfolding Kicking it Off(-shell) with Direct Diffusion

Reference 31

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Observation dad4d17f-1a2d-4c8f-816f-40ba694b467c · outbound

This paper cites Generative Unfolding with Distribution Mapping.

Analysis-ready Generative Unfolding Generative Unfolding with Distribution Mapping

Reference 32

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Observation d536fc29-f47f-4bf1-bbd0-e9adea3bc923 · outbound

This paper cites Machine learning-based event generator for electron-proton scattering.

Analysis-ready Generative Unfolding Machine learning-based event generator for electron-proton scattering

Reference 33

Resolution
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Observation 8924c318-5566-4b7b-a887-b2e2077ef3f6 · outbound

This paper cites Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference.

Analysis-ready Generative Unfolding Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference

Reference 34

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Observation 9730440b-2171-404c-8ed3-2aa1fc86250c · outbound

This paper cites Simulation-Prior Independent Neural Unfolding Procedure.

Analysis-ready Generative Unfolding Simulation-Prior Independent Neural Unfolding Procedure

Reference 35

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Observation 2bec63cf-d4b4-4b8b-92aa-970626852d34 · outbound

This paper cites High-Dimensional Unfolding in Large Backgrounds.

Analysis-ready Generative Unfolding High-Dimensional Unfolding in Large Backgrounds

Reference 36

Resolution
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Observation 36276c7e-8c66-48b5-a0a2-738abedab909 · outbound

This paper cites Understanding Event-Generation Networks via Uncertainties.

Analysis-ready Generative Unfolding Understanding Event-Generation Networks via Uncertainties

Reference 37

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Observation 8d473795-cf4d-40d7-af00-645089d10919 · outbound

This paper cites A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties.

Analysis-ready Generative Unfolding A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties

Reference 38

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source=pdf_text observed=2026-08-15T16:41:36.887022Z digest=sha256:e985cec9c6fbdc4d20ae1fcd3f431d5609a48eb80ab8ace16ef84ff6fa0dae6f

Observation d2f6e197-c3d8-427d-ac6f-2d780d0274fc · outbound

This paper cites How to GAN Event Subtraction.

Analysis-ready Generative Unfolding How to GAN Event Subtraction

Reference 39

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source=pdf_text observed=2026-08-15T16:41:36.891650Z digest=sha256:ee18b829a198cb793a4eae1ce2de4ac7e8f6e08deaf0f6d7cffad9a6bed5f99e

Observation ac02e3e3-393b-4df1-8fc2-6611b2334784 · outbound

This paper cites Residual ANODE.

Analysis-ready Generative Unfolding Residual ANODE

Reference 40

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source=pdf_text observed=2026-08-15T16:41:36.896522Z digest=sha256:eb5b725b3b6b1cf1337ce8f65dbda2b8abf81f8447aad04f118d4c74813d36b8

Observation f5b98bcd-cac4-44f7-832c-7a25c887beff · outbound

This paper cites Generator Based Inference (GBI).

Analysis-ready Generative Unfolding Generator Based Inference (GBI)

Reference 41

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source=pdf_text observed=2026-08-15T16:41:36.901291Z digest=sha256:2ff2aa6904ff05816761844e6bdcf18cdf308ee6b897fe6da32845f8a7628882

Observation f4415e76-2cf7-44f9-a670-11fe51a036f0 · outbound

This paper cites Efficiency Parameterization with Neural Networks.

Analysis-ready Generative Unfolding Efficiency Parameterization with Neural Networks

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-08-15T16:41:37.521488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-15T16:41:36.906101Z digest=sha256:0f168f31a838a3458bf1f633d7f27a2e0522d13fd84a439b87dbad5d45ed9d83

Observation 371226f8-f08f-4636-aeb0-4dd32b5615ef · outbound

This paper cites Precision-Machine Learning for the Matrix Element Method.

Analysis-ready Generative Unfolding Precision-Machine Learning for the Matrix Element Method

Reference 43

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source=pdf_text observed=2026-08-15T16:41:36.910653Z digest=sha256:3180ee42c72eded4e173459aa12716fc22fabf18c89cf5632381bcde2d9ac525

Observation 72312384-1fec-466c-8e38-b43af03aef3b · outbound

This paper cites Flow Matching for Generative Modeling.

Analysis-ready Generative Unfolding Flow Matching for Generative Modeling

Reference 44

Resolution
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no resolver link, observed 2026-08-15T16:41:36.915309Z

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source=pdf_text observed=2026-08-15T16:41:36.915309Z digest=sha256:f13150c56d12838fa957a8f04a9fb548569ff4874f7a650e579b64c5028496ed

Observation 413c9619-efac-4e08-b1ae-0942c21a3385 · outbound

This paper cites Jet Diffusion versus JetGPT -- Modern Networks for the LHC.

Analysis-ready Generative Unfolding Jet Diffusion versus JetGPT -- Modern Networks for the LHC

Reference 45

Resolution
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source=pdf_text observed=2026-08-15T16:41:36.919815Z digest=sha256:6336e614c9039115f0283c0937a2a1262edfb3220722c6be875020829edb7b80

Observation 1e9b39cf-9541-4730-85ff-c2384fae77d6 · outbound

This paper cites EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion.

Analysis-ready Generative Unfolding EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion

Reference 46

Resolution
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no resolver link, observed 2026-08-15T16:41:36.924364Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T16:41:36.924364Z digest=sha256:499eed3f8ac397908d2f68da2e746186a178de4c1df77eaeb900de5b786612a7

Observation 41b6169a-2542-48bf-af7b-42e5f07299ce · outbound

This paper cites Brehmer, V.

Analysis-ready Generative Unfolding Brehmer, V

Reference 47

Resolution
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no resolver link, observed 2026-08-15T16:41:36.929008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.929008Z digest=sha256:07aefcc0fc1c64722d0576266d19ee5eec4feb48cc00ea5afbad976054d3ce48

Observation 1fd31c7a-4c9d-4f90-8e2c-05b651e7dc08 · outbound

This paper cites Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction.

Analysis-ready Generative Unfolding Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction

Reference 48

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no resolver link, observed 2026-08-15T16:41:36.933308Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T16:41:36.933308Z digest=sha256:e7eba6b68023a5ed13c4dbf24a741815c78c7f75a522f4a5989a3913a9824093

Observation 9e1ba791-3a99-4e77-9501-c412330d7f91 · outbound

This paper cites CaloDREAM -- Detector Response Emulation via Attentive flow Matching.

Analysis-ready Generative Unfolding CaloDREAM -- Detector Response Emulation via Attentive flow Matching

Reference 49

Resolution
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no resolver link, observed 2026-08-15T16:41:36.938143Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T16:41:36.938143Z digest=sha256:0d2149b4e182de8faf11c0827cddba7484c24a04cca1cf71811b97a503d48cc8

Observation cd21bc67-3ec8-4ccd-8480-d3d6c1940eaa · outbound

This paper cites Efficient many-jet event generation with Flow Matching.

Analysis-ready Generative Unfolding Efficient many-jet event generation with Flow Matching

Reference 50

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no resolver link, observed 2026-08-15T16:41:36.942683Z

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source=pdf_text observed=2026-08-15T16:41:36.942683Z digest=sha256:da209dd7f18d307679da1805edc66aae6fa137abd7cbf355b9de0b5d065effad

Observation 5c9ff44f-a63a-4411-a5c1-567541e7477e · outbound

This paper cites An Introduction to PYTHIA 8.2.

Analysis-ready Generative Unfolding An Introduction to PYTHIA 8.2

Reference 51

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no resolver link, observed 2026-08-15T16:41:36.947457Z

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source=pdf_text observed=2026-08-15T16:41:36.947457Z digest=sha256:8f04431dab0e6d4b94aeb0393080c262c7c2091d39d71492981d833cace6b003

Observation a28ce4dc-2f53-41f0-a9b4-a34eb87199ad · outbound

This paper cites Herwig++ Physics and Manual.

Analysis-ready Generative Unfolding Herwig++ Physics and Manual

Reference 52

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no resolver link, observed 2026-08-15T16:41:36.952210Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T16:41:36.952210Z digest=sha256:0463c171754ae3a6d33161d276135deebafd1cfaf9e999fd3a6bd97bc3e652c4

Observation 090b8537-9bf2-419f-9a5e-f0c2b7f1643b · outbound

This paper cites Reweighting Parton Showers.

Analysis-ready Generative Unfolding Reweighting Parton Showers

Reference 53

Resolution
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no resolver link, observed 2026-08-15T16:41:36.956866Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T16:41:36.956866Z digest=sha256:34c4e233f827cead612b00eef1755123fbfd7fbe5b109b7f3b32886a79c3bbde

Observation 14657f9f-0702-46d8-9449-3fcb4c0d7800 · outbound

This paper cites Herwig 7.1 Release Note.

Analysis-ready Generative Unfolding Herwig 7.1 Release Note

Reference 54

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no resolver link, observed 2026-08-15T16:41:36.961546Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T16:41:36.961546Z digest=sha256:0dbe8404546d3d1f036dd79afd98cfb3e17ad66b0dba692d02e3e264cd017723

Observation eeae09bc-df68-4030-b2ae-d3b9bf2ad4d5 · outbound

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

Analysis-ready Generative Unfolding DELPHES 3, A modular framework for fast simulation of a generic collider experiment

Reference 55

Resolution
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no resolver link, observed 2026-08-15T16:41:36.966272Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T16:41:36.966272Z digest=sha256:110a9ae24e3cf63fbec381f68a527c56a75d01d9fb3132f855d40883aeff3e22

Observation 20ccdff7-db3c-4db2-be37-b1c0800bed9a · outbound

This paper cites FastJet user manual.

Analysis-ready Generative Unfolding FastJet user manual

Reference 56

Resolution
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no resolver link, observed 2026-08-15T16:41:36.971060Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.971060Z digest=sha256:bb21f0253215a1d4f8f146ad25bf3f6965c070af86129e2d1b2496f203ba6677

Observation c48cb997-722b-45a6-8184-bb98cc0fb0e6 · outbound

This paper cites The anti-k_t jet clustering algorithm.

Analysis-ready Generative Unfolding The anti-k_t jet clustering algorithm

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-15T16:41:36.976061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.976061Z digest=sha256:74fa2e544a99851c5621ed1b1ca69053a0c79facf05ec151cf934f96f5816a67

Observation 9f97c791-b8b6-4eb0-8a4c-abde97384b3b · outbound

This paper cites Jet Trimming.

Analysis-ready Generative Unfolding Jet Trimming

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-15T16:41:36.980663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.980663Z digest=sha256:6e808ada85353ce721b08dfe9d5709967740c3cdeb227448159c2b7bd9cbcaa5

Observation 311d0a3b-f29a-41ed-bc02-f52e6a176dfc · outbound

This paper cites Recombination Algorithms and Jet Substructure: Pruning as a Tool for Heavy Particle Searches.

Analysis-ready Generative Unfolding Recombination Algorithms and Jet Substructure: Pruning as a Tool for Heavy Particle Searches

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-15T16:41:36.985299Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.985299Z digest=sha256:41b3c42e16ef05aa3a34c321d2a7b2f18bb8c7936b1774119c22f0ca13c8e598

Observation 7eadc8c7-7b63-494d-89d6-36eda8871413 · outbound

This paper cites Techniques for improved heavy particle searches with jet substructure.

Analysis-ready Generative Unfolding Techniques for improved heavy particle searches with jet substructure

Reference 60

Resolution
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no resolver link, observed 2026-08-15T16:41:36.990244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.990244Z digest=sha256:8bc7ef18f793099b9e34e31c5ad49259895c5cb2a5745bc450b8a5d705211a55

Observation 5dc7d7c5-712f-429a-aa9d-616b322e49d7 · outbound

This paper cites Towards an understanding of jet substructure.

Analysis-ready Generative Unfolding Towards an understanding of jet substructure

Reference 61

Resolution
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no resolver link, observed 2026-08-15T16:41:36.995453Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.995453Z digest=sha256:74ff870c505ade23b6910873de84ea4caa2ce30a9d4c13aa99db4dd271cd5480

Observation 598b399c-949a-43cb-84f7-c2e193a1ae10 · outbound

This paper cites Soft Drop.

Analysis-ready Generative Unfolding Soft Drop

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-15T16:41:37.000220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:37.000220Z digest=sha256:5db4fb3793252365ac5ab13d724149532c2db6b4904fc4dca098aea121afae26

Observation 1033600d-9105-4e90-90a4-5964fb85c7bb · outbound

This paper cites Identifying Boosted Objects with N-subjettiness.

Analysis-ready Generative Unfolding Identifying Boosted Objects with N-subjettiness

Reference 63

Resolution
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no resolver link, observed 2026-08-15T16:41:37.004918Z

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source=pdf_text observed=2026-08-15T16:41:37.004918Z digest=sha256:0a6163b77acfffbf1226c6133325e59167a82fdc8260068b5996db21316bd1d1

Observation 671ce155-0696-408a-8cc4-29f979a99bd0 · outbound

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

Analysis-ready Generative Unfolding Maximizing Boosted Top Identification by Minimizing N-subjettiness

Reference 64

Resolution
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no resolver link, observed 2026-08-15T16:41:37.009463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:37.009463Z digest=sha256:0a205b06ac44f6691c2114bb04d3371ea3fc8006350d86d55cd77b4b302a038d

Observation 47c3c0a8-8fab-4832-b048-855efcad7cbd · outbound

This paper cites Tools for Unbinned Unfolding.

Analysis-ready Generative Unfolding Tools for Unbinned Unfolding

Reference 65

Resolution
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no resolver link, observed 2026-08-15T16:41:37.014276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:37.014276Z digest=sha256:a0ecdc84eecb608aacad121c8d49534eb43358fec1a0b69f040bfef0ad6f386f

Observation eef04283-08a2-4911-8849-bd95a7b86379 · outbound

This paper cites Stay Positive: Neural Refinement of Sample Weights.

Analysis-ready Generative Unfolding Stay Positive: Neural Refinement of Sample Weights

Reference 66

Resolution
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no resolver link, observed 2026-08-15T16:41:37.018709Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:37.018709Z digest=sha256:d12ecfe6c823cf11001ca6cfaba5003afc020ab497803932c601d01ad5ed7f95

Pith citing papers

Observation fd4b3ef6-2f15-4029-a0f1-6959f89ce059 · inbound

Profiling systematic uncertainties in Simulation-Based Inference with Factorizable Normalizing Flows cites this paper.

Profiling systematic uncertainties in Simulation-Based Inference with Factorizable Normalizing Flows Analysis-ready Generative Unfolding

Reference 31

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no resolver link, observed 2026-08-02T23:36:55.617103Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T23:36:55.617103Z digest=sha256:6c9429bafea4d44ab93144aea37ea52e11a7e32ca8534e270bc28ab22a729a4d

Observation bc47cbd2-ba80-476a-9462-52114e6367e5 · inbound

Factorizable Normalizing Flows for parameter-dependent density morphing cites this paper.

Factorizable Normalizing Flows for parameter-dependent density morphing Analysis-ready Generative Unfolding

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-07-01T15:25:47.360645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T03:41:22.092575Z digest=sha256:ee903cd7e8d7426b9dc8c77abe7355aef6c4e33d8ecdf422a465491a04a7b5d2