Calomplification — the power of generative calorimeter models. (22nd September 2022)

• Record Type: Journal Article
• Authors: Bieringer, S.; Butter, A.; Diefenbacher, S.; Eren, E.; Gaede, F.; Hundhausen, D.; Kasieczka, G.; Nachman, B.; Plehn, T.; Trabs, M.
• Is Part Of: Journal of instrumentation. Volume 17:Number 9(2022)
• Publication Date: 2022-09-22
• Subjects: Detector modelling and simulations I (interaction of radiation with matter, interaction of photons with matter, interaction of hadrons with matter, etc) -- Simulation methods and programs -- Analysis and statistical methods -- Calorimeter methods; Scientific apparatus and instruments -- Periodicals; 502.84
• DOI: 10.1088/1748-0221/17/09/P09028
• Languages: English
• ISSNs: 1748-0221

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 an electromagnetic calorimeter.