Learning Standard Model structure from LHC data with Riemannian flow matching | Paper Citation Record

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This paper cites An Introduction to PYTHIA 8.2.

Learning Standard Model structure from LHC data with Riemannian flow matching An Introduction to PYTHIA 8.2

Reference 1

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This paper cites Herwig 7.0 / Herwig++ 3.0 Release Note.

Learning Standard Model structure from LHC data with Riemannian flow matching Herwig 7.0 / Herwig++ 3.0 Release Note

Reference 2

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This paper cites Event Generation with Sherpa 2.2.

Learning Standard Model structure from LHC data with Riemannian flow matching Event Generation with Sherpa 2.2

Reference 3

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This paper cites The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations.

Learning Standard Model structure from LHC data with Riemannian flow matching The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations

Reference 4

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This paper cites Matching NLO QCD computations and parton shower simulations.

Learning Standard Model structure from LHC data with Riemannian flow matching Matching NLO QCD computations and parton shower simulations

Reference 5

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This paper cites A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX.

Learning Standard Model structure from LHC data with Riemannian flow matching A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX

Reference 6

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This paper cites WHIZARD: Simulating Multi-Particle Processes at LHC and ILC.

Learning Standard Model structure from LHC data with Riemannian flow matching WHIZARD: Simulating Multi-Particle Processes at LHC and ILC

Reference 7

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Learning Standard Model structure from LHC data with Riemannian flow matching Unresolved cited work

Reference 8

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This paper cites Denby, Neural Networks and Cellular Automata in Experimental High-energy Physics, Comput.

Learning Standard Model structure from LHC data with Riemannian flow matching Denby, Neural Networks and Cellular Automata in Experimental High-energy Physics, Comput

Reference 9

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Observation 4f72cf91-59d8-45c7-99e7-80a0b9f834ab · outbound

This paper cites Graph Neural Networks in Particle Physics.

Learning Standard Model structure from LHC data with Riemannian flow matching Graph Neural Networks in Particle Physics

Reference 10

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This paper cites Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking.

Learning Standard Model structure from LHC data with Riemannian flow matching Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking

Reference 11

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Observation 08bdc296-12fc-4156-bfee-e12ab68143f2 · outbound

This paper cites Particle Transformer for Jet Tagging.

Learning Standard Model structure from LHC data with Riemannian flow matching Particle Transformer for Jet Tagging

Reference 12

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This paper cites A Living Review of Machine Learning for Particle Physics.

Learning Standard Model structure from LHC data with Riemannian flow matching A Living Review of Machine Learning for Particle Physics

Reference 13

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This paper cites Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters.

Learning Standard Model structure from LHC data with Riemannian flow matching Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters

Reference 14

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Observation 0cde05fd-3f63-463f-8538-9372cd6c0375 · outbound

This paper cites CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows.

Learning Standard Model structure from LHC data with Riemannian flow matching CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows

Reference 15

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This paper cites Score-based Generative Models for Calorimeter Shower Simulation.

Learning Standard Model structure from LHC data with Riemannian flow matching Score-based Generative Models for Calorimeter Shower Simulation

Reference 16

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This paper cites Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation.

Learning Standard Model structure from LHC data with Riemannian flow matching Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation

Reference 17

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This paper cites Amramet al., CaloChallenge 2022: a community chal- lenge for fast calorimeter simulation, Rept.

Learning Standard Model structure from LHC data with Riemannian flow matching Amramet al., CaloChallenge 2022: a community chal- lenge for fast calorimeter simulation, Rept

Reference 18

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This paper cites AtlFast3: the next generation of fast simulation in ATLAS.

Learning Standard Model structure from LHC data with Riemannian flow matching AtlFast3: the next generation of fast simulation in ATLAS

Reference 19

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This paper cites How to GAN LHC Events.

Learning Standard Model structure from LHC data with Riemannian flow matching How to GAN LHC Events

Reference 20

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This paper cites Machine Learning and LHC Event Generation.

Learning Standard Model structure from LHC data with Riemannian flow matching Machine Learning and LHC Event Generation

Reference 21

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This paper cites PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics.

Learning Standard Model structure from LHC data with Riemannian flow matching PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics

Reference 22

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This paper cites Fast Point Cloud Generation with Diffusion Models in High Energy Physics.

Learning Standard Model structure from LHC data with Riemannian flow matching Fast Point Cloud Generation with Diffusion Models in High Energy Physics

Reference 23

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This paper cites PC-Droid: Faster diffusion and improved quality for particle cloud generation.

Learning Standard Model structure from LHC data with Riemannian flow matching PC-Droid: Faster diffusion and improved quality for particle cloud generation

Reference 24

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

Learning Standard Model structure from LHC data with Riemannian flow matching Jet Diffusion versus JetGPT -- Modern Networks for the LHC

Reference 25

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

Learning Standard Model structure from LHC data with Riemannian flow matching EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion

Reference 26

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

Learning Standard Model structure from LHC data with Riemannian flow matching CaloDREAM -- Detector Response Emulation via Attentive flow Matching

Reference 27

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This paper cites Vaselli, F.

Learning Standard Model structure from LHC data with Riemannian flow matching Vaselli, F

Reference 28

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This paper cites Generative models on phase space.

Learning Standard Model structure from LHC data with Riemannian flow matching Generative models on phase space

Reference 29

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Learning Standard Model structure from LHC data with Riemannian flow matching Unresolved cited work

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Learning Standard Model structure from LHC data with Riemannian flow matching Unresolved cited work

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This paper cites Symmetries, Safety, and Self-Supervision.

Learning Standard Model structure from LHC data with Riemannian flow matching Symmetries, Safety, and Self-Supervision

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This paper cites Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models.

Learning Standard Model structure from LHC data with Riemannian flow matching Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models

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This paper cites OmniJet-$\alpha$: The first cross-task foundation model for particle physics.

Learning Standard Model structure from LHC data with Riemannian flow matching OmniJet-$\alpha$: The first cross-task foundation model for particle physics

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Learning Standard Model structure from LHC data with Riemannian flow matching Mikuni and B

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Learning Standard Model structure from LHC data with Riemannian flow matching Bhimji, C

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This paper cites Hsuet al., EveNet: A Foundation Model for Par- ticle Collision Data Analysis (2026), arXiv:2601.17126 [hep-ex].

Learning Standard Model structure from LHC data with Riemannian flow matching Hsuet al., EveNet: A Foundation Model for Par- ticle Collision Data Analysis (2026), arXiv:2601.17126 [hep-ex]

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Learning Standard Model structure from LHC data with Riemannian flow matching A Language Model for Particle Tracking

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Learning Standard Model structure from LHC data with Riemannian flow matching Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics

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Learning Standard Model structure from LHC data with Riemannian flow matching Amram, L

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Learning Standard Model structure from LHC data with Riemannian flow matching Omana Kuttan, K

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Learning Standard Model structure from LHC data with Riemannian flow matching Classification without labels: Learning from mixed samples in high energy physics

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Learning Standard Model structure from LHC data with Riemannian flow matching Anomaly Detection for Resonant New Physics with Machine Learning

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Learning Standard Model structure from LHC data with Riemannian flow matching Anomaly Detection with Density Estimation

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Learning Standard Model structure from LHC data with Riemannian flow matching Classifying Anomalies THrough Outer Density Estimation (CATHODE)

Reference 45

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This paper cites CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transforming Adjacent Intervals.

Learning Standard Model structure from LHC data with Riemannian flow matching CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transforming Adjacent Intervals

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This paper cites Simulation Assisted Likelihood-free Anomaly Detection.

Learning Standard Model structure from LHC data with Riemannian flow matching Simulation Assisted Likelihood-free Anomaly Detection

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Learning Standard Model structure from LHC data with Riemannian flow matching FETA: Flow-Enhanced Transportation for Anomaly Detection

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This paper cites Improving new physics searches with diffusion models for event observables and jet constituents.

Learning Standard Model structure from LHC data with Riemannian flow matching Improving new physics searches with diffusion models for event observables and jet constituents

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This paper cites Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows.

Learning Standard Model structure from LHC data with Riemannian flow matching Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows

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Learning Standard Model structure from LHC data with Riemannian flow matching Metzger, L

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

Learning Standard Model structure from LHC data with Riemannian flow matching The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics

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

Learning Standard Model structure from LHC data with Riemannian flow matching Machine Learning in the Search for New Fundamental Physics

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Learning Standard Model structure from LHC data with Riemannian flow matching Machine Learning for Anomaly Detection in Particle Physics

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Learning Standard Model structure from LHC data with Riemannian flow matching Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector

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Learning Standard Model structure from LHC data with Riemannian flow matching Search for new phenomena in two-body invariant mass distributions using unsupervised machine learning for anomaly detection at $\sqrt{s} = 13$ TeV with the ATLAS detector

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Learning Standard Model structure from LHC data with Riemannian flow matching Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at $\sqrt{s}$ = 13 TeV

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Learning Standard Model structure from LHC data with Riemannian flow matching Flow Matching on General Geometries

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Learning Standard Model structure from LHC data with Riemannian flow matching Attention Is All You Need

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Learning Standard Model structure from LHC data with Riemannian flow matching Unresolved cited work

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Learning Standard Model structure from LHC data with Riemannian flow matching Unresolved cited work

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This paper cites Navaset al.(Particle Data Group), Review of particle physics, Phys.

Learning Standard Model structure from LHC data with Riemannian flow matching Navaset al.(Particle Data Group), Review of particle physics, Phys

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Learning Standard Model structure from LHC data with Riemannian flow matching Measurement of the differential cross-sections of inclusive, prompt and non-prompt J/psi production in proton-proton collisions at sqrt(s) = 7 TeV

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Observation 1c605ff4-1c33-41e5-963d-e53a7fdb7545 · outbound

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Learning Standard Model structure from LHC data with Riemannian flow matching Measurement of Upsilon production in 7 TeV pp collisions at ATLAS

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Learning Standard Model structure from LHC data with Riemannian flow matching Measurement of the Drell--Yan triple-differential cross section in $pp$ collisions at $\sqrt{s} = 8$ TeV

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Observation 66a370c7-8929-4687-9593-00b763ed131b · outbound

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Learning Standard Model structure from LHC data with Riemannian flow matching Weinberg, A Model of Leptons, Phys

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Learning Standard Model structure from LHC data with Riemannian flow matching Unresolved cited work

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Learning Standard Model structure from LHC data with Riemannian flow matching Unresolved cited work

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Observation 61305696-dac5-4a58-8832-ac589e7ad518 · outbound

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Learning Standard Model structure from LHC data with Riemannian flow matching Kullback and R

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Observation f718e69f-6e6a-477d-b107-ebc92517f4aa · outbound

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Learning Standard Model structure from LHC data with Riemannian flow matching Jeffreys,Theory of Probability, 2nd ed

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Learning Standard Model structure from LHC data with Riemannian flow matching Unresolved cited work

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Learning Standard Model structure from LHC data with Riemannian flow matching Evaluating generative models in high energy physics

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Learning Standard Model structure from LHC data with Riemannian flow matching How to Understand Limitations of Generative Networks

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Learning Standard Model structure from LHC data with Riemannian flow matching Refereeing the Referees: Evaluating Two-Sample Tests for Validating Generators in Precision Sciences

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Learning Standard Model structure from LHC data with Riemannian flow matching Electron and photon energy calibration with the ATLAS detector using LHC Run 1 data

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Learning Standard Model structure from LHC data with Riemannian flow matching Muon reconstruction performance of the ATLAS detector in proton--proton collision data at $\sqrt{s}$=13 TeV

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Learning Standard Model structure from LHC data with Riemannian flow matching The performance of missing transverse momentum reconstruction and its significance with the ATLAS detector using 140 fb$^{-1}$ of $\sqrt{s}=13$ TeV $pp$ collisions

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Learning Standard Model structure from LHC data with Riemannian flow matching Search for an invisibly decaying Higgs boson or dark matter candidates produced in association with a $Z$ boson in $pp$ collisions at $\sqrt{s} =$ 13 TeV with the ATLAS detector

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Observation bc0a8cff-87f7-4729-9319-5dcaee09240c · outbound

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Learning Standard Model structure from LHC data with Riemannian flow matching Unresolved cited work

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Observation c9db89e6-07e0-4ed4-9c17-4952ae88aa14 · outbound

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Learning Standard Model structure from LHC data with Riemannian flow matching Measurement of the weak mixing angle using the forward-backward asymmetry of Drell-Yan events in pp collisions at 8 TeV

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Observation 4edf6cd5-554c-4763-92b9-fe0fbaf4e98e · outbound

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Learning Standard Model structure from LHC data with Riemannian flow matching Measurement of the Drell-Yan forward-backward asymmetry and of the effective leptonic weak mixing angle in proton-proton collisions at $\sqrt{s}$ = 13 TeV

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:43.150258Z

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Observation ea97630e-f40b-4210-9533-dca4abbe18cf · outbound

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Learning Standard Model structure from LHC data with Riemannian flow matching Measurement of the top quark mass in the $t\bar{t}\to$ lepton+jets channel from $\sqrt{s}=8$ TeV ATLAS data and combination with previous results

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Observation 7913788f-959b-470a-bdb8-29da230de8ea · outbound

This paper cites A likelihood-based reconstruction algorithm for top-quark pairs and the KLFitter framework.

Learning Standard Model structure from LHC data with Riemannian flow matching A likelihood-based reconstruction algorithm for top-quark pairs and the KLFitter framework

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Observation 00c277bd-7472-4826-ae47-ccb0f76a3cd3 · outbound

This paper cites Search for Scalar Diphoton Resonances in the Mass Range $65-600$ GeV with the ATLAS Detector in $pp$ Collision Data at $\sqrt{s}$ = 8 $TeV$.

Learning Standard Model structure from LHC data with Riemannian flow matching Search for Scalar Diphoton Resonances in the Mass Range $65-600$ GeV with the ATLAS Detector in $pp$ Collision Data at $\sqrt{s}$ = 8 $TeV$

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:43.439769Z

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Observation 0b8c87a5-494a-4e15-8124-4a0949450a8b · outbound

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Learning Standard Model structure from LHC data with Riemannian flow matching Skwarnicki,A study of the radiative cascade transi- tions between the Upsilon-prime and Upsilon resonances, Ph.D

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Learning Standard Model structure from LHC data with Riemannian flow matching Higgs boson production cross-section measurements and their EFT interpretation in the $4\ell$ decay channel at $\sqrt{s}$ = 13 TeV with the ATLAS detector

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Observation 088c90e0-212d-431d-86d2-e372e57f0d6b · outbound

This paper cites Flow Matching for Generative Modeling.

Learning Standard Model structure from LHC data with Riemannian flow matching Flow Matching for Generative Modeling

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Observation 725fb3c9-7bfc-4a2f-9a3b-2c3169086db3 · outbound

This paper cites Shoemake, Animating Rotation with Quaternion Curves, inProc.

Learning Standard Model structure from LHC data with Riemannian flow matching Shoemake, Animating Rotation with Quaternion Curves, inProc

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Observation 6b9971ff-fd85-4931-b0d5-c5ba00f333f0 · outbound

This paper cites Scalable Diffusion Models with Transformers.

Learning Standard Model structure from LHC data with Riemannian flow matching Scalable Diffusion Models with Transformers

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Observation 98321406-5be5-4f5a-947b-1feb5238f84e · outbound

This paper cites Root Mean Square Layer Normalization.

Learning Standard Model structure from LHC data with Riemannian flow matching Root Mean Square Layer Normalization

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Learning Standard Model structure from LHC data with Riemannian flow matching Unresolved cited work

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Observation 83a64547-fc59-4ff6-b69e-5303d40a5b55 · outbound

This paper cites Decoupled Weight Decay Regularization.

Learning Standard Model structure from LHC data with Riemannian flow matching Decoupled Weight Decay Regularization

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Observation 82695d9f-13fc-4906-9fcd-7a7f8a840947 · outbound

This paper cites Software and computing for Run 3 of the ATLAS experiment at the LHC.

Learning Standard Model structure from LHC data with Riemannian flow matching Software and computing for Run 3 of the ATLAS experiment at the LHC

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