High energy physics experiments rely heavily on the detailed detector simulation models in many tasks. Running these detailed models typically requires a notable amount of the computing time available to the experiments. In this work, we demonstrate a new approach to speed up the simulation of the Time Projection Chamber tracker of the MPD experiment at the NICA accelerator complex. Our method is based on a Generative Adversarial Network – a deep learning technique allowing for implicit estimation of the population distribution for a given set of objects. This approach lets us learn and then sample from the distribution of raw detector responses, conditioned on the parameters of the charged particle tracks. To evaluate the quality of the proposed model, we integrate a prototype into the MPD software stack and demonstrate that it produces high-quality events similar to the detailed simulator, with a speed-up of at least an order of magnitude. The prototype is trained on the responses from the inner part of the detector and, once expanded to the full detector, should be ready for use in physics tasks.

高能物理实验在许多任务中严重依赖于详细的探测器模拟模型。运行这些详细模型通常需要大量可用于实验的计算时间。在这项工作中，我们展示了一种新方法来加速 NICA 加速器复合体上 MPD 实验的时间投影室跟踪器的模拟。我们的方法基于生成对抗网络——一种深度学习技术，允许隐式估计给定对象集的人口分布。这种方法让我们学习，然后从原始探测器响应的分布中进行采样，条件是带电粒子轨迹的参数。为了评估所提出模型的质量，我们将原型集成到 MPD 软件堆栈中，并证明它产生了类似于详细模拟器的高质量事件，速度至少提高了一个数量级。原型是根据探测器内部的响应进行训练的，一旦扩展到完整的探测器，就可以用于物理任务。