Abstract
The future PANDA experiment at FAIR aims to investigate a wide range of physics processes in antiproton-proton collisions at rates of up to 20 MHz, while employing a purely software-based event filter. To educate the trigger decisions, a full event reconstruction has to be carried out in real time. This challenge is amplified when considering tracks from particles with long lifetimes and displaced decay vertices, which add to the complexity of the reconstruction algorithms. Here, we present modifications to a cellular automatonbased track finder taking detector time-stamps into account in addition to spatial information, as well as several pattern recognition methods for longitudinal track reconstrucion with PANDA’s Straw Tube Tracker.
Highlights
The future PANDA experiment at FAIR aims to investigate a wide range of physics processes in antiproton-proton collisions at rates of up to 20 MHz, while employing a purely software-based event filter
The future PANDA experiment (Antiproton Annihilation at Darmstadt) [1] opens up a wide range of antiproton induced reactions with a multi-purpose detector that has a coverage of nearly 4π solid angle
Antiproton beams with high intensity and momenta of up to 15 GeV/c will be provided by the High Energy Storage Ring (HESR)
Summary
The future PANDA experiment (Antiproton Annihilation at Darmstadt) [1] opens up a wide range of antiproton induced reactions with a multi-purpose detector (see fig. 1) that has a coverage of nearly 4π solid angle. The free-streaming data from the detector will be grouped by beam revolutions of HESR, each taking circa 2000 ns Within these bunches, the reconstruction algorithms have to group hits into tracks, and into events. For the spatial reconstruction of the trajectory the STT consists of a number of straws precisely aligned parallel to the beam and magnetic field, which measure the helix circle. Each of the two semi-cylindrical PANDA-STT volumes is filled by three sectors of straw tubes aligned in the z-direction and arranged in stacks of planar tion for the 3-dimensional track reconstruction. Using a cellular automaton as part of a track reconstruction algorithm differs from its common use, where typically new generations of the state vector are created based on a predefined set of rules referring to specific neighbourhood relations. The additional computational footprint of taking into account the temporal hit data is even smaller, adding ≈ 1 − 2% to the total reconstruction time, depending on the collision rate
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