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One of the most important problems of data processing in high energy andnuclear physics is the event reconstruction. Its main part is the trackreconstruction procedure which consists in looking for all tracks thatelementary particles leave when they pass through a detector among a hugenumber of points, so-called hits, produced when flying particles fire detectorcoordinate planes. Unfortunately, the tracking is seriously impeded by thefamous shortcoming of multiwired, strip and GEM detectors due to appearance inthem 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 fortrue hits, one faces with the quite serious difficulty to unravel possibletrack-candidates via true hits ignoring fakes. We introduce a renewed methodthat is a significant improvement of our previous two-stage approach based onhit preprocessing using directed K-d tree search followed a deep neuralclassifier. We combine these two stages in one by applying recurrent neuralnetwork that simultaneously determines whether a set of points belongs to atrue track or not and predicts where to look for the next point of track on thenext coordinate plane of the detector. We show that proposed deep network ismore accurate, faster and does not require any special preprocessing stage.Preliminary results of our approach for simulated events of the BM@N GEMdetector are presented.

Catch and Prolong: recurrent neural network for seeking track-candidates