Big Data Challenges in High Energy Physics Experiments: The ATLAS (CERN) Fast TracKer Approach

We live in the era of “Big Data” problems. Massive amounts of data are produced and captured, data that require significant amounts of filtering to be processed in a realistically useful form. An excellent example of a “Big Data” problem is the data processing flow in High Energy Physics experiments, in our case the ATLAS detector in CERN. In the Large Hadron Collider (LHC) 40 million collisions of bunches of protons take place every second, which is about 15 trillion collisions per year. For the ATLAS detector alone 1 Mbyte of data is produced for every collision or 2000 Tbytes of data per year. Therefore what is needed is a very efficient real-time trigger system to filter the collisions (events) and identify the ones that contain “interesting” physics for processing. One of the upgrades of the ATLAS Trigger system is the Fast TracKer system. The Fast TracKer is a real-time pattern matching machine able to reconstruct the tracks of the particles in the inner silicon detector of the ATLAS experiment in less than 100 μsec. The Fast TracKer receives the events (collision information) at a rate of 100 kHz from the level 1 trigger, selects the events based on preselected track patterns and then provides the selected data to the High Level Trigger at a rate of ~1 kHz. To achieve this performance the Fast TracKer is made of 8 different types of custom designed boards with 8000 ASICs and 2000 FPGAs. Pattern matching and reconstruction is a common data processing problem and therefore the hardware and algorithms developed for the Fast TracKer can be exploited in applications outside High Energy Physics. This is one of the targets of the Marie Curie IAPP Fast TracKer project: to explore the potentials of the Fast TracKer hardware in applications that are beyond its initial design purpose (e.g. biomedical applications, cognitive image processing and security applications).