一种基于霍夫变换多维扩展的新型径迹寻找算法
A new track finding algorithm based on a multi-dimensional extension of the Hough Transform
AI总结：
提出一种基于多维霍夫变换的径迹寻找算法，执行时间与击中数成正比，适用于高占用率场景，且可适应不同探测器几何结构并支持并行硬件实现，为未来对撞机触发提供有力工具。
摘要：
我们提出了一种新的模式识别算法，用于高能物理实验中的径迹寻找，该算法基于将霍夫变换扩展到多个维度。该算法的一个显著特性是执行时间与待处理击中点的总数成正比，这使得它在高占用率情况下特别有吸引力。该算法需要使用足够大的模拟径迹集进行训练。相同的径迹寻找算法可用于非常不同的探测器几何结构，只需更换用于训练的模拟径迹集即可。该算法的特殊结构也天然适合并行硬件实现，结合其固有的灵活性，应为未来对撞机的触发系统提供最强大的工具。
英文摘要：
We introduce a new pattern recognition algorithm for track finding in High Energy Physics Experiments based on an extension of the Hough Transform to multiple dimensions. A remarkable property of this algorithm is that the execution time is simply proportional to the total number of the hits to be processed, making it particularly attractive for high occupancy situations. The algorithm needs to be trained using a sufficiently large set of simulated tracks. The same track finding algorithm can be used for very different detector geometries and only the set of simulated tracks used for training needs to be changed. The particular structure of the algorithm also lends itself naturally to parallel hardware implementations which, combined with its intrinsic flexibility, should provide a most powerful tool for triggering at future colliders.
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