Machine Learning Acceleration for Fast Triggers

现代粒子物理实验产生了海量的数据，远远超过了可能存储的数量。像CMS和Dune这样的实验已经建立了快速、复杂的数据处理系统，可以识别数据中感兴趣的事件，并将它们保存以供分析。然而，这些系统都有局限性，这可能会影响实验测量的精度。机器学习算法为提高数据选择(或“触发”)系统的性能提供了令人兴奋的可能性。对于粒子物理实验来说，这些算法通常不够快，但新一代快速、可编程的处理设备可能会足够快，从而使它们变得有用。在这个项目中，我们将评估最新一代设备对这些实验的适用性，以及开发足够快和足够高的机器学习算法，以提高CMS和沙丘的物理覆盖范围。

Modern particle physics experiments generate vast amounts of data; far more than can possibly be stored. Experiments such as CMS and DUNE have built fast, complex, data processing systems, that can identify interesting events in the data, and save them for analysis. However these systems have limitations which can impact the precision of the measurements made by the experiment. Machine learning algorithms offer an exciting possibility to improve the performance of the data selection (or "trigger") systems. These algorithms are not typically fast enough for particle physics experiments, but a new generation of fast, programmable, processing devices may speed them up sufficiently to be useful. In this project we will evaluate the suitability of latest generation devices for these experiments, as well as developing machine learning algorithms which are fast enough, and performant enough, to improve the physics reach of CMS and DUNE.