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Paper Citation Record · LEDGER

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation

As of 16 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 7 inbound Pith citation observations for arXiv:2509.07700.

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

pith.paper-citation-record.v1
2509.07700 v1

Coverage vector

measured 72 of 72 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T21:53:55.004838Z

measured 79 of 79 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T13:27:36.639781Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

72 of 72 outbound references displayed

  • verified exact1
  • verified fuzzy49
  • unresolved22
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation c8917e92-c384-4120-82ef-2ccb92b2d45b · outbound

This paper cites Geant4—a simulation toolkit.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Geant4—a simulation toolkit

Reference 1

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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 6e082fb1-47b9-4885-91bf-5903f0481574 · outbound

This paper cites Technical report, CERN, Geneva, 2022.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Technical report, CERN, Geneva, 2022

Reference 2

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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 1ebe7b90-e711-4d22-a5bb-14fb0b253f58 · outbound

This paper cites CMS Phase-2 Computing Model: Update Document.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation CMS Phase-2 Computing Model: Update Document

Reference 3

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Source-reported events for the cited work

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Observation 56185a3b-50fb-4c60-beb8-2ce01333951a · outbound

This paper cites Calogan: Simulating 3d high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Calogan: Simulating 3d high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks

Reference 4

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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 5df85540-3c01-4dbc-a608-966baa36c70f · outbound

This paper cites Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks

Reference 5

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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 4162b675-75f8-49f1-9e2d-764113cab12f · outbound

This paper cites Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network

Reference 6

Resolution
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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 4e2f83d5-438f-4869-8d70-82c6c301af61 · outbound

This paper cites Fast simulation of a high granularity calorimeter by generative adversarial networks.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Fast simulation of a high granularity calorimeter by generative adversarial networks

Reference 7

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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 09f2336f-de26-4246-9d24-eac64063e56a · outbound

This paper cites an unresolved cited work.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Unresolved cited work

Reference 8

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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 ff54110d-1db6-4186-bb9d-fd5988979aea · outbound

This paper cites Deep generative models for fast shower simulation in ATLAS.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Deep generative models for fast shower simulation in ATLAS

Reference 9

Resolution
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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 bc9c37ca-8069-444f-8799-34bbe1b1ace2 · outbound

This paper cites Buhmann, S.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Buhmann, S

Reference 10

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Source-reported events for the cited work

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Observation 6ed4844c-37c1-4480-bef9-0be8bc2f7875 · outbound

This paper cites Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Hum- berto Reyes-Gonzalez, Marco Letizia, and Anthony L.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Hum- berto Reyes-Gonzalez, Marco Letizia, and Anthony L

Reference 11

Resolution
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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 ed01a85f-881e-4b48-ab93-e913ae1debf4 · outbound

This paper cites New angles on fast calorimeter shower simulation.Mach.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation New angles on fast calorimeter shower simulation.Mach

Reference 12

Resolution
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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 40c84e7d-b056-4236-a167-b33ebce680b9 · outbound

This paper cites Metahep: Meta learning for fast shower simulation of high energy physics experiments.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Metahep: Meta learning for fast shower simulation of high energy physics experiments

Reference 13

Resolution
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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 f0912d3c-e661-4162-9269-a52dad5f5bcc · outbound

This paper cites Transformers for generalized fast shower simulation.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Transformers for generalized fast shower simulation

Reference 14

Resolution
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 7c19b09d-15a2-4fcb-a6ef-eb94128f2388 · outbound

This paper cites Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation

Reference 15

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T21:53:45.144857Z digest=sha256:2a3cd45004530971f5961a232c0854698864a32e94e34c11fe5c15bd7529e516

Observation c6488342-84b1-466b-8d8e-c5de108732c9 · outbound

This paper cites Fast and accurate simulations of calorimeter showers with normalizing flows.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Fast and accurate simulations of calorimeter showers with normalizing flows

Reference 16

Resolution
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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.

source=pdf_text observed=2026-08-04T21:53:45.244905Z digest=sha256:4cad76e6e2594bd738aa622645cf31da0d497e8dd3b5c1f5ee91f5401a5c8680

Observation 1586b3e6-98f8-4606-b888-19d55b495213 · outbound

This paper cites Accelerating accurate simulations of calorimeter showers with normalizing flows and probability density distillation.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Accelerating accurate simulations of calorimeter showers with normalizing flows and probability density distillation

Reference 17

Resolution
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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 5826e3be-e909-4bf9-b0ea-e17a70417aad · outbound

This paper cites L2LFlows: generating high-fidelity 3D calorimeter images.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation L2LFlows: generating high-fidelity 3D calorimeter images

Reference 18

Resolution
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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 747331bc-11de-458e-b458-199c9d47596d · outbound

This paper cites Normalizing Flows for High-Dimensional Detector Simulations.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Normalizing Flows for High-Dimensional Detector Simulations

Reference 19

Resolution
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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 0bf015ae-5a51-4846-9464-ffa192042cc5 · outbound

This paper cites Convolu- tional L2LFlows: generating accurate showers in highly granular calorimeters using convolu- tional normalizing flows.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Convolu- tional L2LFlows: generating accurate showers in highly granular calorimeters using convolu- tional normalizing flows

Reference 20

Resolution
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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 9c6cfafd-d943-4d7a-9dee-b579b56a1168 · outbound

This paper cites Score-based generative models for calorimeter shower simulation.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Score-based generative models for calorimeter shower simulation

Reference 21

Resolution
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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 1f21b553-288d-49df-b620-bab3041b6325 · outbound

This paper cites CaloClouds: fast geometry- independent highly-granular calorimeter simulation.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation CaloClouds: fast geometry- independent highly-granular calorimeter simulation

Reference 22

Resolution
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 90f54671-3599-4b23-a5f1-199648d428b7 · outbound

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

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation

Reference 23

Resolution
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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 be2b391b-65a8-49a5-8e16-b2a9b16405e8 · outbound

This paper cites CaloScore v2: single-shot calorimeter shower simulation with diffusion models.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation CaloScore v2: single-shot calorimeter shower simulation with diffusion models

Reference 24

Resolution
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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 b2cc7042-d93d-4750-be54-ce4c03a6849b · outbound

This paper cites CaloClouds II: ultra-fast geometry-independent highly-granular calorime- ter simulation.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation CaloClouds II: ultra-fast geometry-independent highly-granular calorime- ter simulation

Reference 25

Resolution
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.

source=pdf_text observed=2026-08-04T21:53:46.224757Z digest=sha256:f789f9e22e0ae0b3616067f1b63d8a9436caf0ad2c3b4e784069b5f42c4c0dd3

Observation 2fc20330-3be0-4cbb-afb3-332db76797e1 · outbound

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

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation CaloDREAM – Detector Response Emulation via Attentive flow Matching

Reference 26

Resolution
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 a96b981c-102e-444b-9902-19b57bd0993c · outbound

This paper cites CaloHadronic: a diffusion model for the generation of hadronic showers.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation CaloHadronic: a diffusion model for the generation of hadronic showers

Reference 27

Resolution
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.

source=pdf_text observed=2026-08-04T21:53:46.494849Z digest=sha256:2cd6cc50135422a22837e3907ce744e9dc3cf10f46e9d4688600c3f2e789afa0

Observation 0e18c550-edc5-40cd-9852-1b29fef3db43 · outbound

This paper cites Calochallenge 2022: A community challenge for fast calorimeter simulation.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Calochallenge 2022: A community challenge for fast calorimeter simulation

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-04T21:53:46.576541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 7188a0b5-9e54-42e8-a803-8c392ce9147b · outbound

This paper cites AtlFast3: The Next Generation of Fast Simulation in ATLAS.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation AtlFast3: The Next Generation of Fast Simulation in ATLAS

Reference 29

Resolution
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 1318e3a5-8daf-49fa-9b14-6579bffc879f · outbound

This paper cites Par04 example.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Par04 example

Reference 30

Resolution
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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 4670340e-fc2c-4746-a0da-a309386ec453 · outbound

This paper cites Benedikt et al.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Benedikt et al

Reference 31

Resolution
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.

source=pdf_text observed=2026-08-04T21:53:46.964762Z digest=sha256:2d9d7c375f53f39146a09dab8783c708540233ce03f4b3971a4a60f4bd6f6803

Observation d5dfb226-a542-401d-8f8f-13b60b491c40 · outbound

This paper cites Deep generative models for fast shower simulation in atlas.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Deep generative models for fast shower simulation in atlas

Reference 32

Resolution
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.

source=pdf_text observed=2026-08-04T21:53:47.108053Z digest=sha256:0438c010c955b794b240b75e2d79add932855e4a5c2498e0f7c69683563caa4d

Observation 2c0f4c20-bdfd-4ebf-9129-219ce237283d · outbound

This paper cites Omnijet-α: the first cross-task foundation model for particle physics.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Omnijet-α: the first cross-task foundation model for particle physics

Reference 33

Resolution
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 cd3a6e7a-7f50-4673-a0f6-b5a53ebf7183 · outbound

This paper cites Improving language understanding by generative pre-training.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Improving language understanding by generative pre-training

Reference 34

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Observation 4bf8bceb-d36b-44f7-bd4b-e17fa62c4ba7 · outbound

This paper cites Robust training of vector quantized bottleneck models.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Robust training of vector quantized bottleneck models

Reference 35

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Observation fe168150-8e72-412d-93f7-533654a00e1f · outbound

This paper cites OmniJet-${\alpha}_C$: Learning point cloud calorimeter simulations using generative transformers.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation OmniJet-${\alpha}_C$: Learning point cloud calorimeter simulations using generative transformers

Reference 36

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Observation 3cdb7741-ff5d-4922-8a98-977835d28991 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation On the Opportunities and Risks of Foundation Models

Reference 37

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Observation aeb6f599-33d0-4811-84b4-6cdfbf16c22b · outbound

This paper cites A Generalist Agent.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation A Generalist Agent

Reference 38

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Observation e3ef9629-5afa-4d9b-8046-7e9a6c596e79 · outbound

This paper cites Language models are few-shot learners.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Language models are few-shot learners

Reference 39

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Observation 49bf0c72-8f62-4f21-a4d3-4a75dcd91923 · outbound

This paper cites Attention is all you need.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Attention is all you need

Reference 40

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Observation 28acc76a-7050-46fc-bd78-befa1f6948d9 · outbound

This paper cites High- resolution image synthesis with latent diffusion models.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation High- resolution image synthesis with latent diffusion models

Reference 41

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Observation 8b1c82d7-3e16-4875-8545-27e307dd4c4b · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 42

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Observation da707f20-480e-467a-b72f-ee758dddac2e · outbound

This paper cites Denoising Diffusion Implicit Models.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Denoising Diffusion Implicit Models

Reference 43

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Observation cb71b655-a72b-421c-84c1-6579aecae7f6 · outbound

This paper cites Progressive distillation for fast sampling of diffusion models.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Progressive distillation for fast sampling of diffusion models

Reference 44

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Observation 1f317f29-0be4-492a-980a-6c47b4ae3cf8 · outbound

This paper cites Segment anything.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Segment anything

Reference 45

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Observation c08e1793-83cc-42dc-8737-24563f3a9b19 · outbound

This paper cites Lemurs: Large-scale multi-detector electromagnetic universal representation of showers, September 2025.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Lemurs: Large-scale multi-detector electromagnetic universal representation of showers, September 2025

Reference 46

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Observation e4a6ba67-7da7-47a5-b067-29e67fb91f86 · outbound

This paper cites Lemurs dataset: Large-scale multi- detector electromagnetic universal representation of showers, 2025.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Lemurs dataset: Large-scale multi- detector electromagnetic universal representation of showers, 2025

Reference 47

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Observation 63733dc1-cdff-4299-bdfc-c7977cc9c1e4 · outbound

This paper cites Fast calorimeter simulation challenge 2022 - dataset 2, March 2022.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Fast calorimeter simulation challenge 2022 - dataset 2, March 2022

Reference 48

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Observation 24d43a5b-7f3e-4a0b-9d77-bb406697d8a7 · outbound

This paper cites The Open Data Detector Tracking System.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation The Open Data Detector Tracking System

Reference 49

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Observation ba5a9204-ea48-4c2b-a422-8667082b8448 · outbound

This paper cites Bacchetta et al.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Bacchetta et al

Reference 50

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Observation 5db07200-8bae-46ad-8e8c-d1c35856b07d · outbound

This paper cites Design and performance of the calorimeter system for allegro fcc-ee detector concept.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Design and performance of the calorimeter system for allegro fcc-ee detector concept

Reference 51

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Observation 16b2eb86-6ebc-4f13-91c4-4568c58cfc12 · outbound

This paper cites Aidasoft/dd4hep, October 2018.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Aidasoft/dd4hep, October 2018

Reference 52

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Observation 71452491-11df-47c6-906c-8b32f79b61bb · outbound

This paper cites Elucidating the design space of diffusion-based generative models, 2022.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Elucidating the design space of diffusion-based generative models, 2022

Reference 53

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Observation a248e08e-256e-4566-bb1e-09a6b40d4bd2 · outbound

This paper cites Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole

Reference 54

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Observation 09276f32-4931-425f-a99c-aaaf3f3dfe77 · outbound

This paper cites Interacting particle solutions of fokker–planck equations through gradient–log–density estimation.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Interacting particle solutions of fokker–planck equations through gradient–log–density estimation

Reference 55

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Observation 7c51e052-7f6f-4c2c-b5c8-7292db380214 · outbound

This paper cites Consistency models, 2023.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Consistency models, 2023

Reference 56

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Observation 74a4d327-5470-4234-bae8-168bcc85ca1c · outbound

This paper cites Denoising diffusion probabilistic models.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Denoising diffusion probabilistic models

Reference 57

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Observation 75f30439-e8b6-46ea-b3d1-dfad321419ce · outbound

This paper cites Scalable diffusion models with transformers.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Scalable diffusion models with transformers

Reference 58

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Observation 74d5afea-cc6e-4ac0-92e2-625d3bec5a88 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 59

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Observation 144bac5c-98c8-46ec-8cad-13091df34201 · outbound

This paper cites Consistency models.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Consistency models

Reference 60

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Observation 31d6bfb0-4fb5-484a-975a-d7ab51e3329f · outbound

This paper cites Decoupled weight decay regularization, 2019.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Decoupled weight decay regularization, 2019

Reference 61

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Observation 32a810c7-7139-45bc-9e5c-d38a06a5ee5b · outbound

This paper cites MiniCPM: Unveiling the potential of small language models with scalable training strategies.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation MiniCPM: Unveiling the potential of small language models with scalable training strategies

Reference 62

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Observation 3826c817-24dc-4899-9432-de60316b217d · outbound

This paper cites Wang, David Leo Wright Hall, Percy Liang, and Tengyu Ma.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Wang, David Leo Wright Hall, Percy Liang, and Tengyu Ma

Reference 63

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source=pdf_text observed=2026-08-04T21:53:52.814760Z digest=sha256:92712d09edcb0bad29e510322e9b9f80cc3d7748942cb1df2ebc77f2dd12c9e5

Observation 694fd4a4-994a-4742-a249-2c2bf651bd1f · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Pytorch: An imperative style, high-performance deep learning library

Reference 64

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Observation 8450b01c-318c-4aff-b0e4-336fa8787dcb · outbound

This paper cites Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits

Reference 65

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Observation dc243e1a-f52c-4e20-9f35-ef79c4198af5 · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Bootstrap your own latent-a new approach to self-supervised learning

Reference 66

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source=pdf_text observed=2026-08-04T21:53:53.643576Z digest=sha256:105c5872099eb27272f76ab8d55a7daba59f97a0d81511396c4bbbe620aa752f

Observation 2e785995-c3bc-42e5-8e50-75492ce4445c · outbound

This paper cites Level up your performance calculation of the fast shower simulation model.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Level up your performance calculation of the fast shower simulation model

Reference 67

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arxiv_id, observed 2026-08-04T21:53:55.584889Z

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Observation f127d5ab-f512-4d3e-ba29-5bd45931a81f · outbound

This paper cites Evaluating generative models in high energy physics.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Evaluating generative models in high energy physics

Reference 68

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raw_fallback, observed 2026-08-04T21:53:59.171168Z

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Observation e772b5e5-b10c-487b-b991-c975b05d2558 · outbound

This paper cites Improved precision and recall metric for assessing generative models.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Improved precision and recall metric for assessing generative models

Reference 69

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source=pdf_text observed=2026-08-04T21:53:54.424870Z digest=sha256:8660bf8fc8d0116b9a68debb5c981b3813a065e3f504c8c21a2239d863245d2f

Observation 348a81be-3050-44f7-af58-5005e20efa5f · outbound

This paper cites Reliable fidelity and diversity metrics for generative models.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Reliable fidelity and diversity metrics for generative models

Reference 70

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raw_fallback, observed 2026-08-04T21:53:58.574304Z

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source=pdf_text observed=2026-08-04T21:53:54.659925Z digest=sha256:3187e16871c58c7ef26190e238aad63ab5c28729acab5752efdbdec7494ca237

Observation 9d7d1e0d-9789-4aa0-9897-aa56e9cc7de0 · outbound

This paper cites CaloDiT-2.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation CaloDiT-2

Reference 71

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raw_fallback, observed 2026-08-04T21:53:58.084618Z

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 82e743b0-e47e-43ce-a37c-b641cb6c2154 · outbound

This paper cites Aleksa, C.

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation Aleksa, C

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T21:53:57.544898Z

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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Pith citing papers

Observation 0d6aa6c9-bbb7-440e-b409-0626f76bbd2a · inbound

GPT-like transformer model for silicon tracking detector simulation cites this paper.

GPT-like transformer model for silicon tracking detector simulation A Generalisable Generative Model for Multi-Detector Calorimeter Simulation

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-03T13:27:36.639781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T13:27:36.639781Z digest=sha256:7b52edab29cfb9be9196bbe8998cd68ebc3759e80810e6f92a35ad3c624c7dc4

Observation d27160d1-6767-47de-b295-50212713010f · inbound

A universal vision transformer for fast calorimeter simulations cites this paper.

A universal vision transformer for fast calorimeter simulations A Generalisable Generative Model for Multi-Detector Calorimeter Simulation

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-03T12:08:19.003620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T12:08:19.003620Z digest=sha256:62cbf24b98e5befceba8760aa6d67f09f8387a93b5fd62183f8143759965196e

Observation 8f0981b7-51d5-4d88-b6c0-61411831ac9d · inbound

Differentiable Surrogate for Detector Simulation and Design with Diffusion Models cites this paper.

Differentiable Surrogate for Detector Simulation and Design with Diffusion Models A Generalisable Generative Model for Multi-Detector Calorimeter Simulation

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-16T15:28:02.486422Z

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 172ac145-a416-4a15-ba13-bafc6a2c34f4 · inbound

CaloArt: Large-Patch x-Prediction Diffusion Transformers for High-Granularity Calorimeter Shower Generation cites this paper.

CaloArt: Large-Patch x-Prediction Diffusion Transformers for High-Granularity Calorimeter Shower Generation A Generalisable Generative Model for Multi-Detector Calorimeter Simulation

Reference 40

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T04:42:15.876577Z

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 d0b1786e-fd68-4c0a-b62a-620083ab6621 · inbound

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters cites this paper.

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters A Generalisable Generative Model for Multi-Detector Calorimeter Simulation

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-06-28T07:41:44.753192Z

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=2026-06-28T07:41:38.548022Z digest=sha256:a4848e5732497ec2bf52dfd547d0bef0bece227699281ef1053a3f3644e5db27

Observation e788fed5-da7d-481d-a98d-b482470dd808 · inbound

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters cites this paper.

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters A Generalisable Generative Model for Multi-Detector Calorimeter Simulation

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-02T12:30:42.042114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:30:42.042114Z digest=sha256:1f38f2e419946d0fdef7608228a4b7ade1ecfd98cf516a1b9f50490cb30381cd

Observation 5f2a1efa-87c0-4e3b-82eb-030ddc92ee2e · inbound

Lantern: Conflict-Aware Gradient Blending for Physics-Guided Diffusion Models in Calorimeter Simulation cites this paper.

Lantern: Conflict-Aware Gradient Blending for Physics-Guided Diffusion Models in Calorimeter Simulation A Generalisable Generative Model for Multi-Detector Calorimeter Simulation

Reference 42

Resolution
unresolved
no resolver link, observed 2026-07-31T02:22:20.518232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T02:22:20.518232Z digest=sha256:6e7b1e7e73e34e750e41f80e5ea8650a114c1ff11d2328557278d2e8e4299d85