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 approach. 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.

翻译：精确模拟通过探测器进行粒子传输的精确模拟仍然是成功解释高能量物理结果的一个关键要素。 但是,蒙特卡洛模拟在计算资源方面要求极高。 这一挑战促使我们调查更快捷的替代方式,以取代标准的蒙特卡洛方法。 我们采用一种深层学习技术,即General Aversarial Networks(GANs ), 以取代热量计探测器模拟, 并加快模拟时间的量级。 我们遵循了以前的做法,即使用三维共振动神经网络,并开发新的二维共变网络,以更快地解决相同的3D图像生成问题。 此外,我们增加了参数和神经网络的显示能力,获得了更高的精确度。 我们比较了我们最好的共进2D神经网络结构,并对照以前的3D结构以及Geant4数据对它进行了评估。 我们的结果表明了高物理精确度,并进一步巩固了GAN用于快速探测器的模拟。