  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 and GEM detectors due to 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. We introduce a renewed method
that is a significant improvement of our previous two-stage approach based on
hit preprocessing using directed K-d tree search followed a deep neural
classifier. We combine these two stages in one by applying recurrent neural
network that simultaneously determines whether a set of points belongs to a
true track or not and predicts where to look for the next point of track on the
next coordinate plane of the detector. We show that proposed deep network is
more accurate, faster and does not require any special preprocessing stage.
Preliminary results of our approach for simulated events of the BM@N GEM
detector are presented.
