Calorimeter shower superresolution
arXiv:2308.11700 · doi:10.1103/PhysRevD.109.092009
Abstract
Calorimeter shower simulation is a major bottleneck in the Large Hadron Collider computational pipeline. There have been recent efforts to employ deep-generative surrogate models to overcome this challenge. However, many of best performing models have training and generation times that do not scale well to high-dimensional calorimeter showers. In this work, we introduce SuperCalo, a flow-based superresolution model, and demonstrate that high-dimensional fine-grained calorimeter showers can be quickly upsampled from coarse-grained showers. This novel approach presents a way to reduce computational cost, memory requirements and generation time associated with fast calorimeter simulation models. Additionally, we show that the showers upsampled by SuperCalo possess a high degree of variation. This allows a large number of high-dimensional calorimeter showers to be upsampled from much fewer coarse showers with high-fidelity, which results in additional reduction in generation time.
16 pages, 13 figures, v3: title changed, matches published version
References in corpus (36)
- Array Programming with NumPy
- CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
- Anomaly Detection with Density Estimation
- 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
- Classifying Anomalies THrough Outer Density Estimation (CATHODE)
- 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
- 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
- Fast Point Cloud Generation with Diffusion Models in High Energy Physics
- CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
- Targeting Multi-Loop Integrals with Neural Networks
- MadNIS -- Neural Multi-Channel Importance Sampling
- L2LFlows: Generating High-Fidelity 3D Calorimeter Images
- Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows
- Measuring QCD Splittings with Invertible Networks
- Towards a Computer Vision Particle Flow
- ν-Flows: Conditional Neutrino Regression
- Deep generative models for fast photon shower simulation in ATLAS
- Inductive Simulation of Calorimeter Showers with Normalizing Flows
- PC-Droid: Faster diffusion and improved quality for particle cloud generation
- Efficient sampling of constrained high-dimensional theoretical spaces with machine learning
- Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
- -Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows
- ELSA -- Enhanced latent spaces for improved collider simulations
- TopicFlow: Disentangling quark and gluon jets with normalizing flows
- SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments
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- Deep generative models for fast photon shower simulation in ATLAS
- CaloDREAM -- Detector Response Emulation via Attentive flow Matching
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- Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
- CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
- Anomaly detection with flow-based fast calorimeter simulators
- CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry
- Unifying Simulation and Inference with Normalizing Flows
- BitHEP -- The Limits of Low-Precision ML in HEP
- Choose Your Diffusion: Efficient and flexible ways to accelerate the diffusion model in fast high energy physics simulation
- Observable Optimization for Precision Theory: Machine Learning Energy Correlators
- ParaFlow: fast calorimeter simulations parameterized in upstream material configurations