Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network

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 physics information in its latent space. Our understanding of this encoding allows us to propose methods to optimize the generation performance further, for example, by altering latent space sampling or by suggesting specific changes to hyperparameters. In particular, we improve the modeling of the shower shape along the particle incident axis.

Hadrons, Better, Faster, Stronger

“…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.…”

“…A more in-depth discussion on the latent space sampling for the BIB-AE is provided in Ref. [15]. Minibatch discrimination.…”

“…Motivated by the large fraction of resources already consumed by calorimeter simulation [5], and the expected increase due to higher granularities and luminosities, the precise and fast simulation of calorimeters is a primary topic of research [2,[6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. 1 In the quest to simulate calorimeters with high accuracy, standard generative machine leanring methods -Generative Adversarial Networks (GANs) [26], Variational Autoencoders (VAEs) [27] and normalising flows [28][29][30][31] -have all been considered.…”

Calomplification -- The Power of Generative Calorimeter Models

“…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].…”

CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows

“…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.…”