Abstract

An inner tracking system (ITS) based on silicon pixel sensors is currently considered as one of the possible MPD upgrade steps. The main purpose of the new detector is to provide a better precision of the primary and secondary vertex reconstruction and improve track reconstruction in MPD in the region close to the interaction point. To study the ITS performance a new track finding algorithm was developed, which better takes into account the new system’s advantages. In this paper the new algorithm is described and first results obtained on simulated data are presented.

Highlights

  • At present, the accelerator complex NICA [1] is being constructed at JINR (Dubna)

  • As one of the possible MPD upgrade steps, an Inner Tracking System (ITS) based on the generation silicon pixel detectors [3] is being considered to be installed between the beam pipe and the Time Projection Chamber (TPC)

  • A track candidate is a sequence of several hits on different layers of the detector starting from the interaction point, which algorithm considers as belonging to the same track

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Summary

Introduction

The accelerator complex NICA [1] is being constructed at JINR (Dubna). It is intended for performing experiments to study interactions of relativistic nuclei and polarized particles (protons and deuterons). Its simple extension to the ITS is not adequate to fully exploit the potential of the new detector, such a method can not be considered as a good tool to study ITS performance. That is why another algorithm, based on the cellular automaton approach [4], was developed. The main idea of the method is to run a combinatorial search of hit pairs belonging to the same track using apriori constraints to reduce the combinatorics. Such a method should produce good results for tracks with relatively small number of hits per track (as is the case for the stand-alone ITS tracking) and can be efficiently implemented in terms of the processing speed

Detector geometry
Method description
Longitudinal projection
Transverse projection
Algorithm implementation
For each track candidate:
Findings
Summary and plans
Full Text
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