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Found 10 papers, 5 papers with code
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge.
Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
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.
Unifying Simulation and Inference with Normalizing Flows
There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators.
Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
The complete simulation of them in a detector is a computational and storage-intensive task.
Inductive Simulation of Calorimeter Showers with Normalizing Flows
Simulating particle detector response is the single most expensive step in the Large Hadron Collider computational pipeline.
CaloFlow for CaloChallenge Dataset 1
CaloFlow is a new and promising approach to fast calorimeter simulation based on normalizing flows.
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources.
CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows
Recently, we introduced CaloFlow, a high-fidelity generative model for GEANT4 calorimeter shower emulation based on normalizing flows.
CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
We introduce CaloFlow, a fast detector simulation framework based on normalizing flows.
i-flow: High-dimensional Integration and Sampling with Normalizing Flows
We introduce the code i-flow, a python package that performs high-dimensional numerical integration utilizing normalizing flows.
