PILArNet：高能物理中粒子成像液态氩探测器的公共数据集
PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics
摘要：
机器学习解决方案的快速发展往往与测试公共数据集的产生相吻合。这些数据集减少了解决问题的最大障碍-获取数据-同时还提供了比较不同解决方案的基准。此外，大型数据集已被用来训练高性能的功能发现，然后在新的方法中使用的问题超出了最初定义。为了鼓励使用液态氩时间投影室，一类用于高能物理实验的粒子探测器收集的数据分析的快速发展，我们已经产生了PILArNet，第一个2D和3D开放数据集用于一对夫妇的关键分析任务。本文提供的初始数据集包含300，000个以三种不同体积大小模拟和记录的样本。该数据集以稀疏的2D和3D矩阵格式有效地存储，并提供有关体积中模拟颗粒的辅助信息，并可用于公共研究。在本文中，我们描述了数据集，任务和用于获取样本的方法。
英文摘要：
Rapid advancement of machine learning solutions has often coincided with the production of a test public data set. Such datasets reduce the largest barrier to entry for tackling a problem -- procuring data -- while also providing a benchmark to compare different solutions. Furthermore, large datasets have been used to train high-performing feature finders which are then used in new approaches to problems beyond that initially defined. In order to encourage the rapid development in the analysis of data collected using liquid argon time projection chambers, a class of particle detectors used in high energy physics experiments, we have produced the PILArNet, first 2D and 3D open dataset to be used for a couple of key analysis tasks. The initial dataset presented in this paper contains 300,000 samples simulated and recorded in three different volume sizes. The dataset is stored efficiently in sparse 2D and 3D matrix format with auxiliary information about simulated particles in the volume, and is made available for public research use. In this paper we describe the dataset, tasks, and the method used to procure the sample.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
