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Paper Citation Record · LEDGER

Toward an event-level analysis of hadron structure using differential programming

As of 10 August 2026, this Paper Citation Record lists 80 of 80 outbound references and 0 inbound Pith citation observations for arXiv:2507.15768.

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pith.paper-citation-record.v1
2507.15768 v1

Coverage vector

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Outbound references

Observation 8caa4a03-acfa-4572-bb3f-10fd11f710ae · outbound

This paper cites , K}.(8) 6 0.2 0.4 0.6 0.8 x 0.2 0.4 0.6 0.8 y LOITS(4×4) 0.2 0.4 0.6 0.8 x 0.2 0.4 0.6 0.8 y LOITS(4×4) + MH 0.2 0.4 0.6 0.8 x 0.2 0.4 0.6 0.8 y LOITS(50×50) FIG.

Toward an event-level analysis of hadron structure using differential programming , K}.(8) 6 0.2 0.4 0.6 0.8 x 0.2 0.4 0.6 0.8 y LOITS(4×4) 0.2 0.4 0.6 0.8 x 0.2 0.4 0.6 0.8 y LOITS(4×4) + MH 0.2 0.4 0.6 0.8 x 0.2 0.4 0.6 0.8 y LOITS(50×50) FIG

Reference 1

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Toward an event-level analysis of hadron structure using differential programming Unresolved cited work

Reference 2

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Toward an event-level analysis of hadron structure using differential programming , Lfor all theksegments, i.e

Reference 3

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Toward an event-level analysis of hadron structure using differential programming Unresolved cited work

Reference 4

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This paper cites xθ = LI(u;X k, Yk(θ)).(13) Here,X k, Yk(θ) represent the subgrid and corre- sponding local CDF values.

Toward an event-level analysis of hadron structure using differential programming xθ = LI(u;X k, Yk(θ)).(13) Here,X k, Yk(θ) represent the subgrid and corre- sponding local CDF values

Reference 5

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This paper cites half-moon.

Toward an event-level analysis of hadron structure using differential programming half-moon

Reference 6

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This paper cites A multidimensional unfolding method based on bayes’ theorem,.

Toward an event-level analysis of hadron structure using differential programming A multidimensional unfolding method based on bayes’ theorem,

Reference 7

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Toward an event-level analysis of hadron structure using differential programming Factorization of Hard Processes in QCD

Reference 8

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Toward an event-level analysis of hadron structure using differential programming First Monte Carlo global analysis of nucleon transversity with lattice QCD constraints

Reference 9

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Toward an event-level analysis of hadron structure using differential programming Confronting lattice parton distributions with global QCD analysis

Reference 10

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This paper cites New results in the CTEQ-TEA global analysis of parton distributions in the nucleon.

Toward an event-level analysis of hadron structure using differential programming New results in the CTEQ-TEA global analysis of parton distributions in the nucleon

Reference 11

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Toward an event-level analysis of hadron structure using differential programming First global QCD analysis of the TMD $\boldsymbol{g_{1T}}$ from semi-inclusive DIS data

Reference 12

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Toward an event-level analysis of hadron structure using differential programming Combination of aN$^3$LO PDFs and implications for Higgs production cross-sections at the LHC

Reference 13

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Toward an event-level analysis of hadron structure using differential programming Global QCD analysis of spin PDFs in the proton with high-xand lattice constraints,

Reference 16

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Toward an event-level analysis of hadron structure using differential programming NNLO Global Analysis of Polarized Parton Distribution Functions

Reference 17

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Toward an event-level analysis of hadron structure using differential programming Pion Fragmentation Functions at High Energy Colliders

Reference 18

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Toward an event-level analysis of hadron structure using differential programming Bayesian Inferring Nucleon's Gravitation Form Factors via Near-threshold $J/\psi$ Photoproduction

Reference 19

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Toward an event-level analysis of hadron structure using differential programming Determination of unpolarized TMD distributions from the fit of Drell-Yan and SIDIS data at N 4LL,

Reference 21

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Toward an event-level analysis of hadron structure using differential programming NNPDFpol2.0: a global determination of polarised PDFs and their uncertainties at next-to-next-to-leading order

Reference 23

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Toward an event-level analysis of hadron structure using differential programming Machine Learning in Nuclear Physics

Reference 25

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Toward an event-level analysis of hadron structure using differential programming Presenting Unbinned Differential Cross Section Results

Reference 26

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Toward an event-level analysis of hadron structure using differential programming Unbinned inclusive cross-section measurements with machine-learned systematic uncertainties,

Reference 27

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Toward an event-level analysis of hadron structure using differential programming Optimal binning of X-ray spectra and response matrix design

Reference 28

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Toward an event-level analysis of hadron structure using differential programming INFERNO: Inference-Aware Neural Optimisation

Reference 29

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Toward an event-level analysis of hadron structure using differential programming PDE-Foam - a probability-density estimation method using self-adapting phase-space binning

Reference 30

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Toward an event-level analysis of hadron structure using differential programming Constraining Effective Field Theories with Machine Learning

Reference 32

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Toward an event-level analysis of hadron structure using differential programming The frontier of simulation-based inference

Reference 33

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Toward an event-level analysis of hadron structure using differential programming Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production

Reference 35

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Toward an event-level analysis of hadron structure using differential programming QCD Theory meets Information Theory,

Reference 36

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Toward an event-level analysis of hadron structure using differential programming Generator Based Inference (GBI)

Reference 37

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Toward an event-level analysis of hadron structure using differential programming Matching NLO QCD computations with Parton Shower simulations: the POWHEG method

Reference 38

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Toward an event-level analysis of hadron structure using differential programming NNLO jet production in neutral and charged current polarized deep inelastic scattering

Reference 40

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Toward an event-level analysis of hadron structure using differential programming PARTONS: PARtonic Tomography Of Nucleon Software. A computing framework for the phenomenology of Generalized Parton Distributions

Reference 41

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Toward an event-level analysis of hadron structure using differential programming Study of deeply virtual Compton scattering at the future Electron-Ion Collider

Reference 42

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Toward an event-level analysis of hadron structure using differential programming MILOU : a Monte-Carlo for Deeply Virtual Compton Scattering

Reference 43

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Observation dee80d2c-8c94-40cf-8836-15a92f51cd96 · outbound

This paper cites The dipole model Monte Carlo generator Sartre 1.

Toward an event-level analysis of hadron structure using differential programming The dipole model Monte Carlo generator Sartre 1

Reference 44

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Observation 34c57429-6d42-4c18-8de4-f689bcf69ab8 · outbound

This paper cites Exclusive vector meson production at an electron-ion collider.

Toward an event-level analysis of hadron structure using differential programming Exclusive vector meson production at an electron-ion collider

Reference 45

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source=pdf_text observed=2026-08-06T15:29:52.650167Z digest=sha256:842f1a61edcdec35454a7425e02528c8e12235ea50d2a9afad4ecd924d74a500

Observation 7f735ee6-72ec-4e36-914d-30ab9fc39364 · outbound

This paper cites $\Upsilon$ photo-production on the proton at the Electron-Ion Collider.

Toward an event-level analysis of hadron structure using differential programming $\Upsilon$ photo-production on the proton at the Electron-Ion Collider

Reference 46

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source=pdf_text observed=2026-08-06T15:29:52.699554Z digest=sha256:7318a645f5afaa0646637f43c36ce095ee2cd7749cfdc5c8cb4b137fe53928d9

Observation 8fcefaea-43d1-47e4-b46c-e0ff39c9a045 · outbound

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

Toward an event-level analysis of hadron structure using differential programming Invertible Networks or Partons to Detector and Back Again

Reference 47

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source=pdf_text observed=2026-08-06T15:29:52.754861Z digest=sha256:3efb6e8349eca05368eb67e87b8334366138831895600f67eccc03f3196a0da1

Observation aac8a96b-dca7-4dbc-beee-3cf68d94d919 · outbound

This paper cites Morphing parton showers with event derivatives.

Toward an event-level analysis of hadron structure using differential programming Morphing parton showers with event derivatives

Reference 48

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source=pdf_text observed=2026-08-06T15:29:52.888505Z digest=sha256:4faaf9c034e4f0c0cfa7ce452049a579464c53648442570f5535b70784dce445

Observation 38c89430-e693-4257-9061-89e76b7e0246 · outbound

This paper cites MadNIS -- Neural Multi-Channel Importance Sampling.

Toward an event-level analysis of hadron structure using differential programming MadNIS -- Neural Multi-Channel Importance Sampling

Reference 49

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source=pdf_text observed=2026-08-06T15:29:53.117259Z digest=sha256:f47c2aaa08b051918a9ed766dd0ccbe029fd0db14b0c94a29d21f63e90dbefad

Observation 12359bfb-094b-4e9d-9e98-d855a56b96c9 · outbound

This paper cites Interpretable deep learning models for the inference and classification of LHC data.

Toward an event-level analysis of hadron structure using differential programming Interpretable deep learning models for the inference and classification of LHC data

Reference 50

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

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Observation f5a4a2c0-161e-49d8-9ed3-948a5dbc3fba · outbound

This paper cites Fitting a Deep Generative Hadronization Model.

Toward an event-level analysis of hadron structure using differential programming Fitting a Deep Generative Hadronization Model

Reference 51

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source=pdf_text observed=2026-08-06T15:29:53.320335Z digest=sha256:895a2c09240021ccb27847a13df79d60e8c87a54d22c4d4b7aef2c260333478b

Observation 9803e8a8-f777-4f3a-9f48-938765241c68 · outbound

This paper cites Accurate Surrogate Amplitudes with Calibrated Uncertainties,.

Toward an event-level analysis of hadron structure using differential programming Accurate Surrogate Amplitudes with Calibrated Uncertainties,

Reference 52

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source=pdf_text observed=2026-08-06T15:29:53.325248Z digest=sha256:2cb8b97412249145ce2f41cf044d3d1a5253a03954339f4f92e6a40114fd760d

Observation da2ce848-80cb-4bdd-a156-05e2923ff9e3 · outbound

This paper cites Differentiable MadNIS-Lite.

Toward an event-level analysis of hadron structure using differential programming Differentiable MadNIS-Lite

Reference 53

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source=pdf_text observed=2026-08-06T15:29:53.330982Z digest=sha256:07950a77cb2fb605af44463124d90d6cc4c6bf9cb31807a54bc2030203c1961b

Observation e6d99507-1a0a-4818-9c73-5aff180e637a · outbound

This paper cites Communicating Likelihoods with Normalising Flows.

Toward an event-level analysis of hadron structure using differential programming Communicating Likelihoods with Normalising Flows

Reference 54

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source=pdf_text observed=2026-08-06T15:29:53.335484Z digest=sha256:f6f73d22a2890a04b833ea1852d207b29b5c09f0a33e379c006dfac14d6eb962

Observation 7b8fdc32-c640-4de9-bac5-559ab7e84503 · outbound

This paper cites Unfolding with Generative Adversarial Networks.

Toward an event-level analysis of hadron structure using differential programming Unfolding with Generative Adversarial Networks

Reference 56

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source=pdf_text observed=2026-08-06T15:29:53.340132Z digest=sha256:c9a40e6856a0937671e0830e0b6cb06bb1ce0781b33df42f41474c512d084852

Observation fc1de196-5e9e-4afa-8bae-38446769c8cf · outbound

This paper cites How to GAN away Detector Effects.

Toward an event-level analysis of hadron structure using differential programming How to GAN away Detector Effects

Reference 57

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source=pdf_text observed=2026-08-06T15:29:53.345257Z digest=sha256:ab09f5c134e229ebd5e184dae094d3a02f210a5c664dbee0bf4048c5f5bac95c

Observation b3458c20-14ba-4b4b-885e-b93b7bec4fca · outbound

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

Toward an event-level analysis of hadron structure using differential programming Learning to Simulate High Energy Particle Collisions from Unlabeled Data

Reference 58

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Observation a16678b7-a316-4e6c-b88b-e4e8eb0414d2 · outbound

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

Toward an event-level analysis of hadron structure using differential programming Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference

Reference 59

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source=pdf_text observed=2026-08-06T15:29:53.354914Z digest=sha256:1a11942c59714b0643cf5b24dfa81b115ca5703793b3803a5dce7c5b2224aa95

Observation d7bc2a84-995c-4686-b8d1-3ad5f7d41b3d · outbound

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

Toward an event-level analysis of hadron structure using differential programming An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training

Reference 60

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source=pdf_text observed=2026-08-06T15:29:53.359423Z digest=sha256:056b3034ee8c620dea87b5f5242372ba732795b09ca2c02559d1eb5e8c298d6e

Observation 0497231c-8d45-4929-bfe8-d98a748f69d6 · outbound

This paper cites Unifying Simulation and Inference with Normalizing Flows.

Toward an event-level analysis of hadron structure using differential programming Unifying Simulation and Inference with Normalizing Flows

Reference 61

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source=pdf_text observed=2026-08-06T15:29:53.363981Z digest=sha256:0b3430c4b046cb283a16fb78e623e50e9f1f5619a1730b62ba04cc9c21d77ba3

Observation 7b50b317-8ec8-44ff-8c45-39b652bafc4b · outbound

This paper cites Improving generative model-based unfolding with Schr¨ odinger bridges,.

Toward an event-level analysis of hadron structure using differential programming Improving generative model-based unfolding with Schr¨ odinger bridges,

Reference 62

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source=pdf_text observed=2026-08-06T15:29:53.368932Z digest=sha256:f66316fa020acd01ab85ea75e090f5f4858a03dfc44c9b1cacb32a15eacb51f2

Observation 1dcfac4d-a21f-40d9-a917-d837006e46e7 · outbound

This paper cites Moment Unfolding.

Toward an event-level analysis of hadron structure using differential programming Moment Unfolding

Reference 63

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source=pdf_text observed=2026-08-06T15:29:53.373942Z digest=sha256:e1f8f970d939822509c15ee058be24e5505d2840762d4dc40d0b8bdbf0f56ac2

Observation 2a4f93cd-d8aa-45bf-aa20-bf9e6b6003ad · outbound

This paper cites Generative Unfolding with Distribution Mapping.

Toward an event-level analysis of hadron structure using differential programming Generative Unfolding with Distribution Mapping

Reference 64

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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=2026-08-06T15:29:53.379318Z digest=sha256:5c57c8b825c474892d32e292f8c07d48222cd2a6e0efdd90e524daab2a47d920

Observation 4734f650-9fee-45a2-bb8a-c5875e39646c · outbound

This paper cites How to Unfold Top Decays.

Toward an event-level analysis of hadron structure using differential programming How to Unfold Top Decays

Reference 65

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source=pdf_text observed=2026-08-06T15:29:53.385682Z digest=sha256:337daee982d401b8fa24a4ff6cbb73e68cd4d40f1a45965f1a563298ba2c3d20

Observation 158e4cfa-463c-483c-81ea-dab93eaa41ce · outbound

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

Toward an event-level analysis of hadron structure using differential programming OmniFold: A Method to Simultaneously Unfold All Observables

Reference 66

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source=pdf_text observed=2026-08-06T15:29:53.391166Z digest=sha256:7a280037b6dcb744589e47b1c3941d4cd211b8345c3744dbef25b6b7b3926894

Observation be7b3542-c141-4ad6-b851-a0e2dded3dce · outbound

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

Toward an event-level analysis of hadron structure using differential programming Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution

Reference 67

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source=pdf_text observed=2026-08-06T15:29:53.396635Z digest=sha256:14899de027527f404eb467d0c1d8fd2a2f7b0c3ae4932f69054322a380db9008

Observation d8d46552-4e5a-4f93-860c-fe11d94ac00a · outbound

This paper cites SwdFold:A Reweighting and Unfolding method based on Optimal Transport Theory.

Toward an event-level analysis of hadron structure using differential programming SwdFold:A Reweighting and Unfolding method based on Optimal Transport Theory

Reference 68

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source=pdf_text observed=2026-08-06T15:29:53.401974Z digest=sha256:09e47fb64b15d696dbd7c72e21492c2d43403583cb242bf93080734f906e18e7

Observation 9ae3a399-4bc2-4f4b-bb46-d4c52474a6a0 · outbound

This paper cites Tools for Unbinned Unfolding.

Toward an event-level analysis of hadron structure using differential programming Tools for Unbinned Unfolding

Reference 69

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Source-reported events for the cited work

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Observation 08be1c6e-8bd3-4c70-bf07-ebc55aecc7d2 · outbound

This paper cites High-Dimensional Unfolding in Large Backgrounds.

Toward an event-level analysis of hadron structure using differential programming High-Dimensional Unfolding in Large Backgrounds

Reference 70

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

source=pdf_text observed=2026-08-06T15:29:53.413939Z digest=sha256:ab0c869be77e966e8cc034b0d80aceb67f5539d650675f0ace7c6e20a9f43a92

Observation 232eb385-a4a4-4cc3-8e39-82c10e2a8610 · outbound

This paper cites CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation,.

Toward an event-level analysis of hadron structure using differential programming CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation,

Reference 74

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source=pdf_text observed=2026-08-06T15:29:53.419431Z digest=sha256:72b759e0555ab82febb231178397f9eee7f55049281730cf669bf8b782852d6f

Observation 46401279-2a82-4d9b-8595-090b6b00f580 · outbound

This paper cites Unbinned Inference with Correlated Events,.

Toward an event-level analysis of hadron structure using differential programming Unbinned Inference with Correlated Events,

Reference 75

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T15:29:53.423861Z digest=sha256:6cd2b1706735e4cd0681c61f090533667988a449b9694568956e3d4d2a8d319d

Observation b979c58e-ff00-4843-ac60-38f913d05be8 · outbound

This paper cites Bayesian Approach to Inverse Problems: an Application to NNPDF Closure Testing.

Toward an event-level analysis of hadron structure using differential programming Bayesian Approach to Inverse Problems: an Application to NNPDF Closure Testing

Reference 77

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source=pdf_text observed=2026-08-06T15:29:53.428826Z digest=sha256:08d5ed2b090bd95b0eb575bff808e6b4aab98d772d5c2370ce169ac50c32f399

Observation d965e71d-9920-4ebc-8e90-d9492535bd12 · outbound

This paper cites New Generation of Parton Distributions with Uncertainties from Global QCD Analysis.

Toward an event-level analysis of hadron structure using differential programming New Generation of Parton Distributions with Uncertainties from Global QCD Analysis

Reference 78

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source=pdf_text observed=2026-08-06T15:29:53.433775Z digest=sha256:4f4cff4de3538249833f980299b991ce0d94a57867189d28933c660a57c61322

Observation 514fa61b-60d2-4bdd-837a-f454bcf8eec7 · outbound

This paper cites First Monte Carlo analysis of fragmentation functions from single-inclusive $e^+ e^-$ annihilation.

Toward an event-level analysis of hadron structure using differential programming First Monte Carlo analysis of fragmentation functions from single-inclusive $e^+ e^-$ annihilation

Reference 80

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source=pdf_text observed=2026-08-06T15:29:53.439059Z digest=sha256:0b8f985b20fae53da408b2474224250becb963089d76de4a3f8f0e1e1d17e1b2

Observation 4fc49457-50db-4065-a78f-b938b916e5b8 · outbound

This paper cites Deeply Virtual Compton Scattering.

Toward an event-level analysis of hadron structure using differential programming Deeply Virtual Compton Scattering

Reference 82

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source=pdf_text observed=2026-08-06T15:29:53.444105Z digest=sha256:c77c344f03cbfbfed81119f7e7138a30488e8b8ffb015185ba1e68fc22ef73aa

Observation 76327628-2148-4e94-993d-34e8f30fbfa4 · outbound

This paper cites Unraveling hadron structure with generalized parton distributions.

Toward an event-level analysis of hadron structure using differential programming Unraveling hadron structure with generalized parton distributions

Reference 83

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source=pdf_text observed=2026-08-06T15:29:53.449040Z digest=sha256:31f810fedb50dddff6180dfff93376ee999e2c86c7a9af7b7f09aee869ff3639

Observation 50862f21-92bc-40b3-8620-1f94753d5f0b · outbound

This paper cites Kernel methods for evolution of generalized parton distributions.

Toward an event-level analysis of hadron structure using differential programming Kernel methods for evolution of generalized parton distributions

Reference 84

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source=pdf_text observed=2026-08-06T15:29:53.454818Z digest=sha256:faa7a32b7f8012e2a16b6d70b8161f08e97ff864720561e5ce8e4f9ffaca1160

Observation 24c717c2-8ec7-4ae1-8290-f0cf67fa79df · outbound

This paper cites Code for prompt numerical computation of the leading order GPD evolution.

Toward an event-level analysis of hadron structure using differential programming Code for prompt numerical computation of the leading order GPD evolution

Reference 85

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T15:29:53.459675Z digest=sha256:9aff0b515a4d685533455ae9e30d522ed7841ccd1d016bd06977e46b8c86d4f6

Observation ad2b46a7-be2a-4fee-a1ad-28c2988ae371 · outbound

This paper cites Revisiting evolution equations for generalised parton distributions.

Toward an event-level analysis of hadron structure using differential programming Revisiting evolution equations for generalised parton distributions

Reference 86

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source=pdf_text observed=2026-08-06T15:29:53.464610Z digest=sha256:afd0700f48e6e5c80571c25b857145dbc2ec1bfe12d675c9e187742428e51c85

Observation bd2facb9-8c01-42fc-97c9-54ff22884655 · outbound

This paper cites The deconvolution problem of deeply virtual Compton scattering.

Toward an event-level analysis of hadron structure using differential programming The deconvolution problem of deeply virtual Compton scattering

Reference 87

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source=pdf_text observed=2026-08-06T15:29:53.470107Z digest=sha256:a4a4d4929ed7c561f7867254f26fc5878d084689819300281cf7e9fb5cc9e765

Observation 760aabe4-6c4a-4391-9bb2-017655a6762c · outbound

This paper cites Shedding light on shadow generalized parton distributions.

Toward an event-level analysis of hadron structure using differential programming Shedding light on shadow generalized parton distributions

Reference 88

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source=pdf_text observed=2026-08-06T15:29:53.475562Z digest=sha256:6245d2ebdb5644bad06ae61ac759daa284522bda888b2cf910c4b4cf69a9d818

Observation a7cbdd81-2427-4bb9-8ef7-84bf7d5092ad · outbound

This paper cites Extraction of the Parton Momentum-Fraction Dependence of Generalized Parton Distributions from Exclusive Photoproduction.

Toward an event-level analysis of hadron structure using differential programming Extraction of the Parton Momentum-Fraction Dependence of Generalized Parton Distributions from Exclusive Photoproduction

Reference 89

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Observation 682430f4-38e9-44f7-9c23-525a4cb3d722 · outbound

This paper cites Flow-based generative models for Markov chain Monte Carlo in lattice field theory.

Toward an event-level analysis of hadron structure using differential programming Flow-based generative models for Markov chain Monte Carlo in lattice field theory

Reference 91

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Observation cffe32bf-f136-4e18-a9d7-259dca8bc299 · outbound

This paper cites Resampling base distributions of normalizing flows,.

Toward an event-level analysis of hadron structure using differential programming Resampling base distributions of normalizing flows,

Reference 92

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verified fuzzy
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Observation 9282113b-abe7-4226-a05e-9ce4879b805b · outbound

This paper cites Accelerating Markov Chain Monte Carlo sampling with diffusion models.

Toward an event-level analysis of hadron structure using differential programming Accelerating Markov Chain Monte Carlo sampling with diffusion models

Reference 93

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Observation e45c3bcd-5b30-4a32-8c02-3734c1b1752f · outbound

This paper cites Generative adversarial nets,.

Toward an event-level analysis of hadron structure using differential programming Generative adversarial nets,

Reference 94

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Observation 7f4e25a8-cefb-420b-a4ed-3766590a349c · outbound

This paper cites SAGIPS: A Scalable Asynchronous Generative Inverse Problem Solver.

Toward an event-level analysis of hadron structure using differential programming SAGIPS: A Scalable Asynchronous Generative Inverse Problem Solver

Reference 95

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

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Observation 4136d5c3-efd4-4a73-a588-538eac5cf082 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Toward an event-level analysis of hadron structure using differential programming Adam: A Method for Stochastic Optimization

Reference 96

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Pith citing papers

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