Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows

Abstract

In the quest to build generative surrogate models as computationally efficient alternatives to rule-based simulations, the quality of the generated samples remains a crucial frontier. So far, normalizing flows have been among the models with the best fidelity. However, as the latent space in such models is required to have the same dimensionality as the data space, scaling up normalizing flows to high dimensional datasets is not straightforward. The prior L2LFlows approach successfully used a series of separate normalizing flows and sequence of conditioning steps to circumvent this problem. In this work, we extend L2LFlows to simulate showers with a 9-times larger profile in the lateral direction. To achieve this, we introduce convolutional layers and U-Net-type connections, move from masked autoregressive flows to coupling layers, and demonstrate the successful modelling of showers in the ILD Electromagnetic Calorimeter as well as Dataset 3 from the public CaloChallenge dataset.

References in corpus (38)

• NICE: Non-linear Independent Components Estimation
• CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• Evaluating generative models in high energy physics
• Resonant anomaly detection without background sculpting
• EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
• PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
• MadNIS -- Neural Multi-Channel Importance Sampling
• L2LFlows: Generating High-Fidelity 3D Calorimeter Images
• FETA: Flow-Enhanced Transportation for Anomaly Detection
• 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
• The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
• OmniJet-$\alpha$: The first cross-task foundation model for particle physics
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• Efficient phase-space generation for hadron collider event simulation
• Jet Diffusion versus JetGPT -- Modern Networks for the LHC
• CaloFlow for CaloChallenge Dataset 1
• Unweighting multijet event generation using factorisation-aware neural networks
• Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
• Unsupervised in-distribution anomaly detection of new physics through conditional density estimation
• Combining Resonant and Tail-based Anomaly Detection
• Calorimeter shower superresolution
• EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
• Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
• CaloShowerGAN, a Generative Adversarial Networks model for fast calorimeter shower simulation
• Towards a data-driven model of hadronization using normalizing flows
• End-to-end simulation of particle physics events with Flow Matching and generator Oversampling
• Anomaly detection with flow-based fast calorimeter simulators
• Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation
• Kicking it Off(-shell) with Direct Diffusion
• Integrating Particle Flavor into Deep Learning Models for Hadronization
• CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models
• Induced Generative Adversarial Particle Transformers
• BUFF: Boosted Decision Tree based Ultra-Fast Flow matching
• Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning

Cited by in corpus (10)

• Differentiable MadNIS-Lite
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
• Accurate Surrogate Amplitudes with Calibrated Uncertainties
• BitHEP -- The Limits of Low-Precision ML in HEP
• Amplitude Uncertainties Everywhere All at Once
• Extrapolating Jet Radiation with Autoregressive Transformers
• 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
• Ultra Fast Calorimeter Simulation with Generative Machine Learning on FPGAs