  One of the most important problems of data processing in high energy and
nuclear physics is the event reconstruction. Its main part is the track
reconstruction procedure which consists in looking for all tracks that
elementary particles leave when they pass through a detector among a huge
number of points, so-called hits, produced when flying particles fire detector
coordinate planes. Unfortunately, the tracking is seriously impeded by the
famous shortcoming of multiwired, strip in GEM detectors due to the appearance
in them a lot of fake hits caused by extra spurious crossings of fired strips.
Since the number of those fakes is several orders of magnitude greater than for
true hits, one faces with the quite serious difficulty to unravel possible
track-candidates via true hits ignoring fakes. On the basis of our previous
two-stage approach based on hits preprocessing using directed K-d tree search
followed by a deep neural classifier we introduce here two new tracking
algorithms. Both algorithms combine those two stages in one while using
different types of deep neural nets. We show that both proposed deep networks
do not require any special preprocessing stage, are more accurate, faster and
can be easier parallelized. Preliminary results of our new approaches for
simulated events are presented.
