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Tracking in high density environments plays an important role in many physicsanalyses at the LHC. In such environments, it is possible that two nearlycollinear particles contribute to the same hits as they travel through theATLAS pixel detector and semiconductor tracker. If the two particles aresufficiently collinear, it is possible that only a single track candidate willbe created, denominated a "merged track", leading to a decrease in trackingefficiency. These proceedings show a possible new technique that uses a boosteddecision tree to classify reconstructed tracks as merged. An application ofthis new method is the recovery of the number of reconstructed tracks in hightransverse momentum three-pronged $\tau$ decays, leading to an increased $\tau$reconstruction efficiency. The observed mistag rate is small.

Identifying Merged Tracks in Dense Environments with Machine Learning