Abstract
The CBM experiment (FAIR/GSI, Darmstadt, Germany) will focus on the measurement of rare probes at interaction rates up to 10MHz with data flow of up to 1 TB/s. It requires a novel read-out and data-acquisition concept with self-triggered electronics and free-streaming data. In this case resolving different collisions is a non-trivial task and event building must be performed in software online. That requires full online event reconstruction and selection not only in space, but also in time, so-called 4D event building and selection. This is a task of the First-Level Event Selection (FLES).The FLES reconstruction and selection package consists of several modules: track finding, track fitting, short-lived particles finding, event building and event selection. The Cellular Automaton (CA) track finder algorithm was adapted towards time-based reconstruction. In this article, we describe in detail the modification done to the algorithm, as well as the performance of the developed time-based CA approach.
Highlights
The CBM experiment [1] is targeted to explore the QCD phase diagram in the region of high baryon densities by investigating nuclear collisions from 2 to 45 AGeV beam energy
The reason for that is that the track finder usually takes as an input raw detector hit measurements at the very first event reconstruction phase, when no data reduction can be done yet
These cells are combined into reconstructed tracks during the Cellular Automaton (CA) track finder evolution — the process of defining neighboring cells, which with high probability belong to the same track
Summary
The CBM experiment [1] is targeted to explore the QCD phase diagram in the region of high baryon densities by investigating nuclear collisions from 2 to 45 AGeV (per nucleon) beam energy. The reason for that is that the track finder usually takes as an input raw detector hit measurements at the very first event reconstruction phase, when no data reduction can be done yet. The CA method profits from building up so-called cells, i.e. short track segments (triplets in the case of CBM) with a higher dimensionality than measurements, before starting the main combinatorial search. These cells are combined into reconstructed tracks during the CA track finder evolution — the process of defining neighboring cells, which with high probability belong to the same track. Let us consider some details of the CBM CA track finder algorithm
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