mlsanityData to 7 Oct 2026

Unifying Simulation and Inference with Normalizing Flows

arXiv 2404.18992 ↗First posted 29 Apr 2024hep-ph hep-ex physics.data-ancited by 6

Not on the map. Why?

Abstract (arXiv)

There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators. We show that these two tasks can be unified by using maximum likelihood estimation (MLE) from conditional generative models for energy regression. Unlike direct regression techniques, the MLE approach is prior-independent and non-Gaussian resolutions can be determined from the shape of the likelihood near the maximum. Using an ATLAS-like calorimeter simulation, we demonstrate this concept in the context of calorimeter energy calibration.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 37 of its 123 references that are in the map. First posted on arXiv 29 Apr 2024.

Builds on

37 of 123 references.

References · 37

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Variational Inference with Normalizing Flowscited by 2,161
  3. 2016Density estimation using Real NVPcited by 2,029
  4. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  5. 2014NICE: Non-linear Independent Components Estimationcited by 1,052
  6. 2017Masked Autoregressive Flow for Density Estimationcited by 680
  7. 2019Neural Spline Flowscited by 590
  8. 2019The frontier of simulation-based inferencecited by 490
  9. 2015MADE: Masked Autoencoder for Distribution Estimationcited by 306
  10. 2018Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flowscited by 217
  11. 2019Learning Likelihoods with Conditional Normalizing Flowscited by 170
  12. 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
25 more
  1. 2020Anomaly Detection with Density Estimationcited by 71
  2. 2017Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeterscited by 55
  3. 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
  4. 2021CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 34
  5. 2019OmniFold: A Method to Simultaneously Unfold All Observablescited by 31
  6. 2019How to GAN away Detector Effectscited by 28
  7. 2020i-flow: High-dimensional Integration and Sampling with Normalizing Flowscited by 26
  8. 2019Neural Density Estimation and Likelihood-free Inferencecited by 23
  9. 2023End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physicscited by 22
  10. 2020Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inferencecited by 21
  11. 2021Generative Networks for Precision Enthusiastscited by 21
  12. 2022An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative trainingcited by 20
  13. 2021Understanding Event-Generation Networks via Uncertaintiescited by 17
  14. 2022CaloFlow for CaloChallenge Dataset 1cited by 14
  15. 2023Improving Generative Model-based Unfolding with Schr"odinger Bridgescited by 13
  16. 2023High-dimensional and Permutation Invariant Anomaly Detectioncited by 12
  17. 2022nu-Flows: Conditional Neutrino Regressioncited by 11
  18. 2023CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimationcited by 10
  19. 2023nu2-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flowscited by 10
  20. 2021Scaffolding Simulations with Deep Learning for High-dimensional Deconvolutioncited by 9
  21. 2023Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimationcited by 8
  22. 2024Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusioncited by 8
  23. 2022End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networkscited by 6
  24. 2023Residual ANODEcited by 4
  25. 2023The Optimal use of Segmentation for Sampling Calorimeterscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2024Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flowscited by 13
  2. 2026AllShowers: One model for all calorimeter showerscited by 3
  3. 2025A First Full Physics Benchmark for Highly Granular Calorimeter Surrogatescited by 2
  4. 2026Factorizable Normalizing Flows for parameter-dependent density morphingcited by 1
  5. 2025Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division historycited by 0
  6. 2025Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inferencecited by 0