Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of single isolated particles produced in high-energy physics collisions. We train neural networks on single-particle shower data at the calorimeter-cell level, and show significant improvements for simulation and reconstruction when using these networks compared to methods which rely on currently-used state-of-the-art algorithms. We define two models: an end-to-end reconstruction network which performs simultaneous particle identification and energy regression of particles when given calorimeter shower data, and a generative network which can provide reasonable modeling of calorimeter showers for different particle types at specified angles and energies. We investigate the optimization of our models with hyperparameter scans. Furthermore, we demonstrate the applicability of the reconstruction model to shower inputs from other detector geometries, specifically ATLAS-like and CMS-like geometries. These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.

中文翻译：

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深度学习量热法：对撞机物理的粒子模拟和重构

使用量热仪阵雨的详细模拟作为训练数据，我们研究了深度学习算法在高能物理碰撞中模拟和重建单个孤立粒子的应用。我们在量热计单元级别对单颗粒淋浴数据进行神经网络训练，与使用当前依赖于最新技术的方法相比，使用这些网络时，在模拟和重建方面显示出显着的改进。我们定义了两个模型：一个端到端的重构网络，当给定量热计淋浴数据时，它可以同时进行粒子识别和粒子的能量回归；一个生成网络，可以为指定角度和能量下不同类型的粒子提供量热计淋浴的合理建模。我们使用超参数扫描研究模型的优化。此外，我们证明了重建模型对其他探测器几何形状（特别是类似ATLAS和CMS几何形状）的淋浴输入的适用性。这些网络可以用作粒子簇模拟和重建的快速和轻量方法，以用于当前和将来在粒子对撞机上的实验。