EPJ Web Conf.
Volume 201, 2019The XXII International Scientific Conference of Young Scientists and Specialists (AYSS-2018)
|Number of page(s)||5|
|Published online||04 February 2019|
Catch and Prolong: recurrent neural network for seeking track-candidates
Joint Institute for Nuclear Research, 6 Joliot-Curie street, Dubna, Moscow region, Russia
2 Sukhoi State Technical University of Gomel, October Ave. 48, Gomel, 246746 Republic of Belarus
* e-mail: email@example.com
Published online: 4 February 2019
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.
Key words: tracking / BM@N experiment / GEM detectors / directed search / recurrent neural networks
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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