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Found 4 papers, 2 papers with code
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.
SuperCalo: Calorimeter shower super-resolution
In this work, we introduce SuperCalo, a flow-based super-resolution model, and demonstrate that high-dimensional fine-grained calorimeter showers can be quickly upsampled from coarse-grained showers.
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.
