Abstract:
The Large Hadron Collider (LHC) is the world's highest energy accelerator with the highest luminosity, which aims to nd answers to the most fundamental questions of particle physics. It leads us to the discoveries of new particles (e.g discovery of Higgs boson nowadays), as predicted by Standard Model and its extensions like supersymmetry. However, coping up with the data produced by the LHC, and nding the interesting events, is not an easy task. LHC produces 1TB of data per second and approximately only 1 event per million is from interesting physics. At this point, looking for an event in the whole data is like trying to nd a needle in the haystack and to nd the interesting events out of the entire data, trigger systems are used. One of the most important part of trigger is tracking, which one needs to identify the tracks made by particles, and to identify the physics objects in the event. In this thesis, we describe an algorithm, to run at one of the LHC detectors, ATLAS, in order to achieve fast tracking. The algorithm is tested, using very high luminosity simulation data both running on hits obtained from the entire detector and also in certain small regions around interesting signals. The performance is shown to be satisfactory both in terms of tracking e ciency and in terms of timing requirements.
