학술논문
Calomplification — the power of generative calorimeter models
Document Type
article
Author
Source
Journal of Instrumentation. 17(09)
Subject
Language
Abstract
Abstract: Motivated by the high computational costs of classical simulations, machine-learned generative models can be extremely useful in particle physics and elsewhere. They become especially attractive when surrogate models can efficiently learn the underlying distribution, such that a generated sample outperforms a training sample of limited size. This kind of GANplification has been observed for simple Gaussian models. We show the same effect for a physics simulation, specifically photon showers in anelectromagnetic calorimeter.