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Paper Citation Record · LEDGER

Analysis-ready Generative Unfolding

As of 16 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 2026-08-16T06:30:59.297886+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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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:cf1782cf43e405dd3ec94e05c04b4209e45001d60bed0529044aba7326ccc378

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=sha256:52281290875da670dfb5faf4c540e082a45846309f0f970f1471fc7b94597c71

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:fa06ba89ddc0f39024581bb93d4d39921c6fcccf14b0e26024aa6dac61b15113

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=sha256:35b2c2419853379278dfbf2b9de7b2ea38db80b94e7afc38111b23dc33d9d76b

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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source=pdf_text observed=2026-08-15T16:41:36.728247Z digest=sha256:4dd2cd851441db258ab9054b5ab350fb6824dedaf78b697d9bb1fcb8fd7e0fc1

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=sha256:03ca27902dc6a3133d502753143dca187f7867f45089ef9df6502a0e988e029c

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:f257ae80b001231696fb8457853b93a5d5d5c1f414c097fc1e7aeef5a92cd152

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=sha256:2e09c27037f5e9427f84c2ef86e4d1231da778e02fa516574c6d05c1dfd5193f

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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source=pdf_text observed=2026-08-15T16:41:36.751405Z digest=sha256:096203e74e3f44ba4819dfe61743ad7adfc62146a6256d5387ead79336bf4614

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:a856609102ed2b7b6cfdaac13edac1a0fcc45b0e1c3216c6ea02916893c245d2

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:fd0e7d55797c9fdfd06fa98f18ba7a1cd8547ad9b8106ea79dd22802da2f1238

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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source=pdf_text observed=2026-08-15T16:41:36.765959Z digest=sha256:15d492cd7ab21276d41310637f42973c6e07e16efac527b952a8271434ac07bf

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:cace5a3124a98e935029e5448e76da0706e803e25a3e573065f809733b025873

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 2026-08-16T06:30:59.297886+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=sha256:526ef317f920ae14a46ca512b87f37980ac116f80f5be4d95025a0ff90d80f13

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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source=pdf_text observed=2026-08-15T16:41:36.785589Z digest=sha256:2b4cd34840bfb7b1ffe762835fe7a3d027aa3d4e7ddc8e211b017a17d94fdf10

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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source=pdf_text observed=2026-08-15T16:41:36.805791Z digest=sha256:68d01bbdc0305ea0d719f17f3cb81485f4728a1cd5b7cfa80017ddbff9e8928e

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=sha256:9b63164edb2d0143416e1d1cb13877ca0646f2e06b975f7a5840bc4c30983418

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

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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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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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

Resolution
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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

Resolution
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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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no resolver link, observed 2026-08-15T16:41:36.877768Z

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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

Resolution
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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

Resolution
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no resolver link, observed 2026-08-15T16:41:36.891650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.891650Z digest=sha256:a413bad8587f82f7739eb2240eb4af6cd1e65ab9511a65a2805613894eabfe6a

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

This paper cites Residual ANODE.

Analysis-ready Generative Unfolding Residual ANODE

Reference 40

Resolution
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no resolver link, observed 2026-08-15T16:41:36.896522Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.896522Z digest=sha256:db974a39234f9269f362ee06d57ee25e533729c43cf8b7214c0cd84ca78716a4

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

This paper cites Generator Based Inference (GBI).

Analysis-ready Generative Unfolding Generator Based Inference (GBI)

Reference 41

Resolution
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no resolver link, observed 2026-08-15T16:41:36.901291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.901291Z digest=sha256:3f432a6f13071bf888dd45d777a05ea2f288d8b852907e63ca2f1af8c9014510

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T16:41:36.906101Z digest=sha256:265c97eb9e605803198117a703531e2860605dec4ba7aefc56f0acc9c33fc0c9

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

Resolution
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no resolver link, observed 2026-08-15T16:41:36.910653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.910653Z digest=sha256:b8d3842cb74f097c9bf3e3a8264e86a5f2f37854c776dcbcb877ca05b031e987

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.915309Z digest=sha256:1b879336519499defd8519c87824199499d1a3e246aa2e3b26547e87bcde952f

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
unresolved
no resolver link, observed 2026-08-15T16:41:36.919815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.919815Z digest=sha256:926105a6d737e0c4ccc929d4d5a4573ed91167018c7d027d3253a96103b813ca

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.924364Z digest=sha256:c9a83b39589d0078ce27408425f0d1c5398ff1de16659e9165f222e52c09327a

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

This paper cites Brehmer, V.

Analysis-ready Generative Unfolding Brehmer, V

Reference 47

Resolution
unresolved
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=sha256:498f0c3d65eebf4a8e5ab428105a0003581f208299bdd81d2fa6856cd15f4a09

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.933308Z digest=sha256:bbe83dd8fe1b40e8e63170fc55701d97c1bb7923aa7056ee64f11fcc1c62fd22

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.938143Z digest=sha256:efa99949d4640d4ab9b677990fa1e46e087ea216e3e13596c250f65d4ac6f36e

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.942683Z digest=sha256:1541da93f9f49076dc04dc66e0409706ce247ca0b155f2be8d1f8359a17f0532

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.947457Z digest=sha256:984d7b7f184848b73a18e5b8522baf6d1db3193cb2c9a584427a81a1ced94643

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

This paper cites Herwig++ Physics and Manual.

Analysis-ready Generative Unfolding Herwig++ Physics and Manual

Reference 52

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.952210Z digest=sha256:98aea61bb7320553d89119d294029ac5a131ae993a8d6b2381c565a321be5d68

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

This paper cites Reweighting Parton Showers.

Analysis-ready Generative Unfolding Reweighting Parton Showers

Reference 53

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.956866Z digest=sha256:3219f2b9ca183e51b17671fa35dd8ef373211bee2603ab0da8cbcdc613293aa8

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.961546Z digest=sha256:af96338f80a9f823285fc4b060bf0592ba2bb0dd3ed40d0f5ef6390d015d150f

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.966272Z digest=sha256:9c141189fd979dbdb8d7aaf262754bf7cc78faf319c27c8674b3101da7993985

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

This paper cites FastJet user manual.

Analysis-ready Generative Unfolding FastJet user manual

Reference 56

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.971060Z digest=sha256:8af43d9803dbfdad089d46aa3591bea55482a1fd0ddbcba87935dc6520201281

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=sha256:6e54b59984a4b73b970eb4fd6c5303a16c68f74aeb3d361f65ce2798dfa65f5e

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=sha256:11a0c210bb86e8053e4a8ed3c068d160548475b7ebf25e082d52206e4e0e35c4

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.985299Z digest=sha256:c59c4fc81ffa7ba11ba7da4c6ffd5cfab32796b7abf638ec48d910f4e6138994

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
unresolved
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:f2b05cdae4b682318a46d45b52371fcc7bc4da3311624207c4e7aaad0934caa6

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:36.995453Z digest=sha256:3d28ec78d9716c938fda907b00cdfba6cb7e251324e6bcb0b5514f73eaf1687d

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:ff1d2e7cb2e5fcce58fc167266b37f22f817afc2060811295d5f0dae6dc45e06

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:37.004918Z digest=sha256:5a78f7941e8ba3ba4f6d97714afc2a1a57561d064bd5811f75da8df5d8af39b2

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
unresolved
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:ed17505178382866728dfe4ec1e6b37751f9330664a3d01e0753e893b4df4643

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
unresolved
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=sha256:3c47160070fa41d94e96820dd6ae957ad1ea0e380dd357577200ca06f2acc2f9

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:41:37.018709Z digest=sha256:0caa7e9fffc26be476be17b7a84ff6ae7eb1fe32db22455e30be5e05a128eb03

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T23:36:55.617103Z digest=sha256:e6d41577918e47a9bc099bd880453981262e60af298762b2402d2760e50e7469

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-30T03:41:22.092575Z digest=sha256:61755a8c63f31056038c0ccd9415fb387321ceb1d783d359a68eb842e212105d