CaloFlow for CaloChallenge Dataset 1

Abstract

CaloFlow is a new and promising approach to fast calorimeter simulation based on normalizing flows. Applying CaloFlow to the photon and charged pion Geant4 showers of Dataset 1 of the Fast Calorimeter Simulation Challenge 2022, we show how it can produce high-fidelity samples with a sampling time that is several orders of magnitude faster than Geant4. We demonstrate the fidelity of the samples using calorimeter shower images, histograms of high-level features, and aggregate metrics such as a classifier trained to distinguish CaloFlow from Geant4 samples.

36 pages, 21 figures, v3: match published version

References in corpus (27)

• Array Programming with NumPy
• On Calibration of Modern Neural Networks
• Normalizing Flows: An Introduction and Review of Current Methods
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
• Event Generation with Normalizing Flows
• Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
• Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network
• AtlFast3: the next generation of fast simulation in ATLAS
• Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
• i-flow: High-dimensional Integration and Sampling with Normalizing Flows
• Score-based Generative Models for Calorimeter Shower Simulation
• Exploring phase space with Neural Importance Sampling
• Invertible Networks or Partons to Detector and Back Again
• CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
• Understanding Event-Generation Networks via Uncertainties
• Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network
• Generative Networks for Precision Enthusiasts
• Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters
• Targeting Multi-Loop Integrals with Neural Networks
• Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
• Two Invertible Networks for the Matrix Element Method
• Measuring QCD Splittings with Invertible Networks
• Inference of cosmic-ray source properties by conditional invertible neural networks
• ν-Flows: Conditional Neutrino Regression
• Deep generative models for fast photon shower simulation in ATLAS
• Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows

Cited by in corpus (16)

• MadNIS -- Neural Multi-Channel Importance Sampling
• The MadNIS Reloaded
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• Precision-Machine Learning for the Matrix Element Method
• Normalizing Flows for High-Dimensional Detector Simulations
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Differentiable MadNIS-Lite
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
• Anomaly detection with flow-based fast calorimeter simulators
• Unifying Simulation and Inference with Normalizing Flows
• BitHEP -- The Limits of Low-Precision ML in HEP
• CaloHadronic: a diffusion model for the generation of hadronic showers
• Observable Optimization for Precision Theory: Machine Learning Energy Correlators
• ParaFlow: fast calorimeter simulations parameterized in upstream material configurations
• CaloClouds3: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation