  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 colliders
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 ambiguity
resolution. In this step, performed at the end of the tracking chain, we select
which tracks candidates should be kept and which must be discarded. 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. Using a
shared-hits-based clustering algorithm, we can efficiently determine which
candidates belong to the same truth particle. Afterwards, we can apply a Neural
Network (NN) to compare those tracks and decide which ones are duplicates and
which ones should be kept. 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. This new approach
was shown to be 15 times faster than the default ACTS algorithm while removing
32 times more duplicates down to less than one duplicated track per event.
