Score-based Generative Models for Calorimeter Shower Simulation

Abstract

Score-based generative models are a new class of generative algorithms that have been shown to produce realistic images even in high dimensional spaces, currently surpassing other state-of-the-art models for different benchmark categories and applications. In this work we introduce CaloScore, a score-based generative model for collider physics applied to calorimeter shower generation. Three different diffusion models are investigated using the Fast Calorimeter Simulation Challenge 2022 dataset. CaloScore is the first application of a score-based generative model in collider physics and is able to produce high-fidelity calorimeter images for all datasets, providing an alternative paradigm for calorimeter shower simulation.

References in corpus (4)

• Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
• Cherenkov Detectors Fast Simulation Using Neural Networks
• Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
• Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations

Cited by in corpus (50)

• Fast Point Cloud Generation with Diffusion Models in High Energy Physics
• CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• Evaluating generative models in high energy physics
• EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
• PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
• L2LFlows: Generating High-Fidelity 3D Calorimeter Images
• How to Understand Limitations of Generative Networks
• CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
• CaloScore v2: Single-shot Calorimeter Shower Simulation with Diffusion Models
• Deep generative models for fast photon shower simulation in ATLAS
• Inductive Simulation of Calorimeter Showers with Normalizing Flows
• The MadNIS Reloaded
• PC-Droid: Faster diffusion and improved quality for particle cloud generation
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• Diffusion Models as Stochastic Quantization in Lattice Field Theory
• CaloFlow for CaloChallenge Dataset 1
• Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
• High-dimensional and Permutation Invariant Anomaly Detection
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• Precision-Machine Learning for the Matrix Element Method
• A high-granularity calorimeter insert based on SiPM-on-tile technology at the future Electron-Ion Collider
• Normalizing Flows for High-Dimensional Detector Simulations
• Calorimeter shower superresolution
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Differentiable MadNIS-Lite
• CaloShowerGAN, a Generative Adversarial Networks model for fast calorimeter shower simulation
• Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
• Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation
• Anomaly detection with flow-based fast calorimeter simulators
• Accurate Surrogate Amplitudes with Calibrated Uncertainties
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
• CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry
• Differentiable Earth Mover's Distance for Data Compression at the High-Luminosity LHC
• PIPPIN: Generating variable length full events from partons
• Unsupervised and lightly supervised learning in particle physics
• Advancing Set-Conditional Set Generation: Diffusion Models for Fast Simulation of Reconstructed Particles
• Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction
• Ultra fast, event-by-event heavy-ion simulations for next generation experiments
• Leveraging Staggered Tessellation for Enhanced Spatial Resolution in High-Granularity Calorimeters
• Toward a foundation model for heavy-ion collision experiments based on point-cloud diffusion
• Extrapolating Jet Radiation with Autoregressive Transformers
• Differentiable Surrogate for Detector Simulation and Design with Diffusion Models
• BitHEP -- The Limits of Low-Precision ML in HEP
• Amplitude Uncertainties Everywhere All at Once
• CaloHadronic: a diffusion model for the generation of hadronic showers
• Observable Optimization for Precision Theory: Machine Learning Energy Correlators
• Choose Your Diffusion: Efficient and flexible ways to accelerate the diffusion model in fast high energy physics simulation
• CaloClouds3: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• Variational inference for pile-up removal at hadron colliders with diffusion models
• ParaFlow: fast calorimeter simulations parameterized in upstream material configurations