2018/11/14 by Dmitriy Baranov, Gennady Ososkov, Pavel Goncharov +1 · 1 citation
Computer Science · Mathematics · #cs.LG #stat.ML #msc:68T10
paper · pdf · doi:10.1051/epjconf/201920105001
5 pages, 1 figure, XXII International Scientific Conference of Young Scientists and Specialists (AYSS-2018), April 23-27, 2018, http://ayss-2018.jinr.ru
arxiv created 2018/11/14 · arxiv updated 2019/02/20
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.