Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations

Abstract

The precise simulation of particle transport through detectors remains a key element for the successful interpretation of high energy physics results. However, Monte Carlo based simulation is extremely demanding in terms of computing resources. This challenge motivates investigations of faster, alternative approaches for replacing the standard Monte Carlo technique. We apply Generative Adversarial Networks (GANs), a deep learning technique, to replace the calorimeter detector simulations and speeding up the simulation time by orders of magnitude. We follow a previous approach which used three-dimensional convolutional neural networks and develop new two-dimensional convolutional networks to solve the same 3D image generation problem faster. Additionally, we increased the number of parameters and the neural networks representational power, obtaining a higher accuracy. We compare our best convolutional 2D neural network architecture and evaluate it versus the previous 3D architecture and Geant4 data. Our results demonstrate a high physics accuracy and further consolidate the use of GANs for fast detector simulations.

Hadrons, better, faster, stronger

“…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] 4 .…”

Ensemble Models for Calorimeter Simulations

“…In the end, the processed transpositions are rotated back to their original positions and merged. A detailed description of the network architectures is available in [8].…”

“…The LAGAN [5], CaloGAN [6], 3DGAN [7], or 2DGAN [8] are examples of GAN models simulating the EM calorimeters with high fidelity and a speedup of several orders of magnitude.…”

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].…”