Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed

Abstract

Accurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calorimeter detectors is a time consuming process and drives the computing needs of large experiments at the LHC and future colliders. Recently, generative machine learning models based on deep neural networks have shown promise in speeding up this task by several orders of magnitude. We investigate the use of a new architecture -- the Bounded Information Bottleneck Autoencoder -- for modelling electromagnetic showers in the central region of the Silicon-Tungsten calorimeter of the proposed International Large Detector. Combined with a novel second post-processing network, this approach achieves an accurate simulation of differential distributions including for the first time the shape of the minimum-ionizing-particle peak compared to a full GEANT4 simulation for a high-granularity calorimeter with 27k simulated channels. The results are validated by comparing to established architectures. Our results further strengthen the case of using generative networks for fast simulation and demonstrate that physically relevant differential distributions can be described with high accuracy.

arXiv comment17 pages, 12 figures

References in corpus (48)

• Adam: A Method for Stochastic Optimization
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Auto-Encoding Variational Bayes
• An overview of gradient descent optimization algorithms
• Improved Training of Wasserstein GANs
• NICE: Non-linear Independent Components Estimation
• Improved Techniques for Training GANs
• Variational Inference with Normalizing Flows
• Density estimation using Real NVP
• Layer Normalization
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Normalizing Flows for Probabilistic Modeling and Inference
• Anomaly Detection with Density Estimation
• Adversarial Autoencoders
• Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
• Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
• Searching for New Physics with Deep Autoencoders
• A Kernel Method for the Two-Sample Problem
• Anomaly Detection for Resonant New Physics with Machine Learning
• The Machine Learning Landscape of Top Taggers
• Parameterized Machine Learning for High-Energy Physics
• Event Generation with Normalizing Flows
• How to GAN LHC Events
• QCD or What?
• Simulation Assisted Likelihood-free Anomaly Detection
• Novelty Detection Meets Collider Physics
• Learning representations of irregular particle-detector geometry with distance-weighted graph networks
• Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector
• Variational Autoencoders for New Physics Mining at the Large Hadron Collider
• Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network
• JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
• Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
• Exploring phase space with Neural Importance Sampling
• Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
• Fast and accurate simulation of particle detectors using generative adversarial networks
• DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
• How to GAN away Detector Effects
• Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
• Deep Learning as a Parton Shower
• Lund jet images from generative and cycle-consistent adversarial networks
• Using neural networks for efficient evaluation of high multiplicity scattering amplitudes
• Reweighting a parton shower using a neural network: the final-state case
• Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer
• Flows for simultaneous manifold learning and density estimation
• Efficient Monte Carlo Integration Using Boosted Decision Trees and Generative Deep Neural Networks
• LHC analysis-specific datasets with Generative Adversarial Networks
• Information bottleneck through variational glasses

Cited by in corpus (64)

• Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group
• Machine Learning and LHC Event Generation
• Score-based Generative Models for Calorimeter Shower Simulation
• CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
• Understanding Event-Generation Networks via Uncertainties
• Event Generators for High-Energy Physics Experiments
• Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network
• Generative Networks for Precision Enthusiasts
• A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties
• Fast Point Cloud Generation with Diffusion Models in High Energy Physics
• CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
• PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
• Calomplification -- The Power of Generative Calorimeter Models
• L2LFlows: Generating High-Fidelity 3D Calorimeter Images
• Learning to Simulate High Energy Particle Collisions from Unlabeled Data
• How to Understand Limitations of Generative Networks
• Two Invertible Networks for the Matrix Element Method
• Deep generative models for fast photon shower simulation in ATLAS
• Inductive Simulation of Calorimeter Showers with Normalizing Flows
• Towards a Deep Learning Model for Hadronization
• The MadNIS Reloaded
• CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows
• PC-Droid: Faster diffusion and improved quality for particle cloud generation
• OmniJet-$\alpha$: The first cross-task foundation model for particle physics
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• CaloFlow for CaloChallenge Dataset 1
• Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
• Precision-Machine Learning for the Matrix Element Method
• A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission
• A Lorentz-Equivariant Transformer for All of the LHC
• A high-granularity calorimeter insert based on SiPM-on-tile technology at the future Electron-Ion Collider
• Normalizing Flows for High-Dimensional Detector Simulations
• Parameter Estimation using Neural Networks in the Presence of Detector Effects
• Calorimeter shower superresolution
• Differentiable MadNIS-Lite
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Morphological Classification of Radio Galaxies with wGAN-supported Augmentation
• Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks
• CaloShowerGAN, a Generative Adversarial Networks model for fast calorimeter shower simulation
• Studying the potential of Graphcore IPUs for applications in Particle Physics
• Graph Generative Models for Fast Detector Simulations in High Energy Physics
• Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation
• Accurate Surrogate Amplitudes with Calibrated Uncertainties
• CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry
• Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations
• Variational Autoencoders for Jet Simulation
• TURBO: The Swiss Knife of Auto-Encoders
• How to GAN Event Unweighting
• Leveraging Staggered Tessellation for Enhanced Spatial Resolution in High-Granularity Calorimeters
• Denoising Convolutional Networks to Accelerate Detector Simulation
• Foundations of automatic feature extraction at LHC--point clouds and graphs
• Latent Space Refinement for Deep Generative Models
• Extrapolating Jet Radiation with Autoregressive Transformers
• Amplitude Uncertainties Everywhere All at Once
• BitHEP -- The Limits of Low-Precision ML in HEP
• Synthesis of pulses from particle detectors with a Generative Adversarial Network (GAN)
• CaloHadronic: a diffusion model for the generation of hadronic showers
• Ultra-Fast Generation of Air Shower Images for Imaging Air Cherenkov Telescopes using Generative Adversarial Networks
• CaloClouds3: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• ParaFlow: fast calorimeter simulations parameterized in upstream material configurations
• Data-driven detector signal characterization with constrained bottleneck autoencoders