2021
DOI: 10.1051/epjconf/202125103003
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Abstract: Given the increasing data collection capabilities and limited computing resources of future collider experiments, interest in using generative neural networks for the fast simulation of collider events is growing. In our previous study, the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture for generating photon showers in a high-granularity calorimeter showed a high accuracy modeling of various global differential shower distributions. In this work, we investigate how the BIB-AE encodes this phy… Show more

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Cited by 31 publications
(40 citation statements)
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“…In Refs. [14,15] we showed a precise modelling of differential distributions over many orders of magnitude for electromagnetic showers. The present work extends this level of precision for the first time to the more challenging hadron-induced showers in a highly granular hadronic calorimeter.…”
Section: Introductionmentioning
confidence: 92%
See 2 more Smart Citations
“…In Refs. [14,15] we showed a precise modelling of differential distributions over many orders of magnitude for electromagnetic showers. The present work extends this level of precision for the first time to the more challenging hadron-induced showers in a highly granular hadronic calorimeter.…”
Section: Introductionmentioning
confidence: 92%
“…A more in-depth discussion on the latent space sampling for the BIB-AE is provided in Ref. [15]. Minibatch discrimination.…”
Section: Bounded Information Bottleneck Autoencodermentioning
confidence: 99%
See 1 more Smart Citation
“…The goal of this paper is to study the statistical amplification of deep generative models, focusing on interpolation from the smoothness inductive bias, for detector simulations as a realistic and highly relevant application. Fast surrogate models for detector simulations have been developed [13][14][15][16][17][18][19][20][21][22][23][24][25] and improved [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] to the level that they are ready to be used in the upcoming LHC runs. In fact, the ATLAS Collaboration has already integrated a Generative Adversarial Network (GAN) into its fast calorimeter simulation and will use it to generate over a billion events [41,42].…”
Section: Introductionmentioning
confidence: 99%
“…Recently, deep generative modeling has demonstrated great potential to speed up the most computationally expensive part of detector simulations, namely calorimeter showers [6][7][8][9][10][11][12][13][14][15][16][17]. By fitting the generative model to Geant4 shower images, the generative model learns (often implicitly) the underlying distribution that the Geant4 showers are drawn from and can then sample from it quickly.…”
Section: Introductionmentioning
confidence: 99%