Performance of a Real-time Multipurpose 2-Dimensional Clustering Algorithm Developed for the ATLAS Experiment

In this paper the performance of the 2D pixel clustering algorithm developed for the Input Mezzanine card of the ATLAS Fast TracKer system is presented. Fast TracKer is an approved ATLAS upgrade that has the goal to provide a complete list of tracks to the ATLAS High Level Trigger for each level-1 accepted event, at up to 100 kHz event rate with a very small latency, in the order of 100 µs. The Input Mezzanine card is the input stage of the Fast TracKer system. Its role is to receive data from the silicon detector and perform real time clustering, thus to reduce the amount of data propagated to the subsequent processing levels with minimal information loss. We focus on the most challenging component on the Input Mezzanine card, the 2D clustering algorithm executed on the pixel data. We compare two different implementations of the algorithm. The first is one called the ideal one which searches clusters of pixels in the whole silicon module at once and calculates the cluster centroids exploiting the whole available information, included the precise sharing of charge produced by the particle between contiguous pixels of the cluster. The second one uses a sliding window technique to identify clusters of contiguous pixels, one at a time. In addition, a simplified centre of mass is calculated as the center of a bounding box which contains the cluster. The size of the window sets a limit to the maximum cluster that can be bound, so clusters can be split if their sizes exceeds the window one. We show that the simplified implementation saves a large amount of hardware resources and has the equivalent performance for the use in the Fast TracKer processor. Finally, we describe an event display that is a powerful diagnostic/monitoring tool used to understand in detail the performance of the algorithm, also used during the data taking.