  Tracking in high-density environments, such as the core of TeV jets, is
particularly challenging both because combinatorics quickly diverge and because
tracks may not leave anymore individual "hits" but rather large clusters of
merged signals in the innermost tracking detectors. In the CMS collaboration,
this problem has been addressed in the past with cluster splitting algorithms,
working layer by layer, followed by a pattern recognition step where a high
number of candidate tracks are tested. Modern Deep Learning techniques can be
used to better handle the problem by correlating information on multiple layers
and directly providing proto-tracks without the need of an explicit cluster
splitting algorithm. Preliminary results will be presented with ideas on how to
further improve the algorithms.
