Abstract
Highly precise simulations of elementary particles interaction and processes are fundamental toaccurately reproduce and interpret the experimental results in High Energy Physics (HEP) detectorsand to correctly reconstruct the particle flows. Today, detector simulations typically rely on MonteCarlo-based methods which are extremely demanding in terms of computing resources. The need forsimulated data at future experiments - like the ones that will run at the High Luminosity Large HadronCollider (HL-LHC) - are expected to increase by orders of magnitude, increasing drastically thecomputational challenge. This expectation motivates the research for alternative deep learning-basedsimulation strategies.In this research we speed-up HEP detector simulations for the specific case of calorimeters usingGenerative Adversarial Networks (GANs) with a huge factor of over 150 000x compared to thestandard Monte Carlo simulations. This could only be achieved by designing smart convolutional 2Dnetwork architectures for generating 3D images representing the detector volume. Detailed physicsevaluation shows an accuracy similar to the Monte Carlo simulation.Furthermore, we quantize the data format for the neural network architecture (float32) with the IntelLow Precision Optimization tool (LPOT) to a reduced precision (int8) data format. This results in anadditional 1.8x speed-up on modern Intel hardware while maintaining the physics accuracy. Theseexcellent results consolidate the beneficial use of GANs for future fast detector simulations
Highlights
Detector simulations are primarily performed with the Geant4 toolkit [1] which relies on Monte Carlo-based methods
In this research are Generative Adversarial Networks (GANs) - a modern Deep Learning approach - applied to speed-up calorimeter simulations
We evaluate the Conv2D GAN model in terms of physics accuracy and computational speed and compare it to a previous architecture taken from Ref. [10] which uses Conv3D layers for the same simulation use case
Summary
Detector simulations are primarily performed with the Geant toolkit [1] which relies on Monte Carlo-based methods. In this research are Generative Adversarial Networks (GANs) - a modern Deep Learning approach - applied to speed-up calorimeter simulations. The second loss (named AUX, for AUXiliary loss) represents the result of a regression task on the initial particle energy Ep, that the discriminator estimates from the images using a dense layer. It is implemented as a Mean Absolute Percentage Error (MAPE) [9]. The third discriminator output comes from a Lambda layer, calculating the sum over the pixels of the input image which, corresponds to the total energy of the input image It is entitled ECAL and uses the MAPE loss function likewise
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