CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds

Abstract

Precision measurements and new physics searches at the Large Hadron Collider require efficient simulations of particle propagation and interactions within the detectors. The most computationally expensive simulations involve calorimeter showers. Advances in deep generative modelling - particularly in the realm of high-dimensional data - have opened the possibility of generating realistic calorimeter showers orders of magnitude more quickly than physics-based simulation. However, the high-dimensional representation of showers belies the relative simplicity and structure of the underlying physical laws. This phenomenon is yet another example of the manifold hypothesis from machine learning, which states that high-dimensional data is supported on low-dimensional manifolds. We thus propose modelling calorimeter showers first by learning their manifold structure, and then estimating the density of data across this manifold. Learning manifold structure reduces the dimensionality of the data, which enables fast training and generation when compared with competing methods.

Accepted to the Machine Learning and the Physical Sciences Workshop at NeurIPS 2022

References in corpus (6)

• AtlFast3: the next generation of fast simulation in ATLAS
• Score-based Generative Models for Calorimeter Shower Simulation
• Normalizing Flows on Riemannian Manifolds
• CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows
• Density estimation on low-dimensional manifolds: an inflation-deflation approach
• Diagnosing and Fixing Manifold Overfitting in Deep Generative Models

Cited by in corpus (17)

• EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
• MadNIS -- Neural Multi-Channel Importance Sampling
• L2LFlows: Generating High-Fidelity 3D Calorimeter Images
• How to Understand Limitations of Generative Networks
• CaloScore v2: Single-shot Calorimeter Shower Simulation with Diffusion Models
• 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
• Differentiable MadNIS-Lite
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Accurate Surrogate Amplitudes with Calibrated Uncertainties
• LHC Hadronic Jet Generation Using Convolutional Variational Autoencoders with Normalizing Flows
• Extrapolating Jet Radiation with Autoregressive Transformers
• BitHEP -- The Limits of Low-Precision ML in HEP
• Amplitude Uncertainties Everywhere All at Once