  Tracking is one of the most time consuming aspects of event reconstruction at
the Large Hadron Collider (LHC) and its high-luminosity upgrade (HL-LHC).
Innovative detector technologies extend tracking to four-dimensions by
including timing in the pattern recognition and parameter estimation. However,
present and future hardware already have additional information that is largely
unused by existing track seeding algorithms. The shape of clusters provides an
additional dimension for track seeding that can significantly reduce the
combinatorial challenge of track finding. We use neural networks to show that
cluster shapes can reduce significantly the rate of fake combinatorical
backgrounds while preserving a high efficiency. We demonstrate this using the
information in cluster singlets, doublets and triplets. Numerical results are
presented with simulations from the TrackML challenge.
