The reconstruction of particle trajectories is a key challenge of particle physics experiments, as it directly impacts particle identification and physics performances while also representing one of the main CPU consumers of many high energy physics experiments. As the luminosity of particle collider increases, this reconstruction will become more challenging and resource intensive. New algorithms are thus needed to address these challenges efficiently. One potential step of track reconstruction is the ambiguity resolution. In this step, performed at the end of the tracking chain, we select which tracks candidates should to be kept and which ones need to be discarded. In the ATLAS experiment, for example, this is achieved by identifying fakes tracks, removing duplicates and determining via a Neural Network which hits should be shared by multiple tracks. The speed of this algorithm is directly driven by the number of track candidates, which can be reduced at the cost of some physics performance. Since this problem is fundamentally an issue of comparison and classification, we propose to use a machine learning based approach to the Ambiguity Resolution itself. Using a nearest neighbour search, we can efficiently determine which candidates belong to the same truth particle. Afterward, we can apply a Neural Network (NN) to compare those tracks and determine which ones are the duplicate and which one should be kept. Finally, another NN is applied to all the remaining candidates to identify which ones are fakes and remove those. This approach is implemented within A Common Tracking Software (ACTS) framework and tested on the Open Data Detector (ODD) a realistic virtual detector, similar to a future ATLAS one, to fully evaluate the potential of this approach.
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