Forecasting Generative Amplification
Not on the map. Why?
Abstract (arXiv)
Generative networks are perfect tools to enhance the speed and precision of LHC simulations. Especially when generating events beyond the size of the training dataset, it is important to understand their statistical precision. We present two complementary methods to estimate the amplification factor without large holdout datasets. Averaging amplification uses Bayesian networks or ensembling to estimate amplification from the precision of integrals over given phase-space volumes. Differential amplification uses hypothesis testing to quantify amplification without any resolution loss. Applied to state-of-the-art event generators, both methods indicate that amplification is already possible in specific regions of phase space.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 34 of its 92 references that are in the map. First posted on arXiv 9 Sep 2025.
Builds on
34 of 92 references.
References · 34
- 2021Repulsive Deep Ensembles are Bayesiancited by 103
- 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
- 2017Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeterscited by 55
- 2022Score-based Generative Models for Calorimeter Shower Simulationcited by 52
- 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
- 2019Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physicscited by 42
- 2024Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physicscited by 36
- 2021CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 34
- 2024CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulationcited by 34
- 2023CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 33
- 2020GANplifying Event Samplescited by 28
- 2022CaloMan: Fast generation of calorimeter showers with density estimation on learned manifoldscited by 28
22 more
- 2024A Lorentz-Equivariant Transformer for All of the LHCcited by 27
- 2019LHC analysis-specific datasets with Generative Adversarial Networkscited by 26
- 2023Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoningcited by 25
- 2020DCTRGAN: Improving the Precision of Generative Models with Reweightingcited by 22
- 2023Inductive Simulation of Calorimeter Showers with Normalizing Flowscited by 22
- 2021Generative Networks for Precision Enthusiastscited by 21
- 2020Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)cited by 20
- 2021Understanding Event-Generation Networks via Uncertaintiescited by 17
- 2024Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flowscited by 13
- 2025Lorentz Local Canonicalization: How to Make Any Network Lorentz-Equivariantcited by 12
- 2023Goodness of fit by Neyman-Pearson testingcited by 10
- 2021Optimising simulations for diphoton production at hadron colliders using amplitude neural networkscited by 9
- 2024Refereeing the Referees: Evaluating Two-Sample Tests for Validating Generators in Precision Sciencescited by 8
- 2025CaloHadronic: a diffusion model for the generation of hadronic showerscited by 8
- 2024PIPPIN: Generating variable length full events from partonscited by 6
- 2024Calibrating Bayesian Generative Machine Learning for Bayesiamplificationcited by 5
- 2024Optimal Equivariant Architectures from the Symmetries of Matrix-Element Likelihoodscited by 5
- 2024Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactionscited by 4
- 2025Comparing Generative Models with the New Physics Learning Machinecited by 4
- 2024Extrapolating Jet Radiation with Autoregressive Transformerscited by 2
- 2024An information theoretic limit to data amplificationcited by 2
- 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
Built on by
Citing papers per year: 2026: 3.
3 papers.