Abstract

The future PANDA experiment at FAIR experiment aims to cover a wide range of processes in antiproton-proton collisions at event rates of up to 20 MHz. Such event rates make reconstruction a challenging task for the purely software-based event filter. Investigating complex event topologies with displaced vertices increases the difficulty even further. Here we present two attempts to meet these future challenges: an algorithm for track reconstruction based on pattern matching with pre-determined look-up tables, and as a continuation of this approach a system of neural networks for identifying specific particle track candidates and predicting their momentum.

Highlights

  • The future PANDA experiment at FAIR experiment aims to cover a wide range of processes in antiproton-proton collisions at event rates of up to 20 MHz

  • The High Energy Storage Ring (HESR) will deliver high intensity antiproton beams with momenta of up to 15 GeV/c and a nearly continuous beam structure

  • Amongst the many processes PANDA will study, there are those with challenging topologies involving secondary vertices

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Summary

Track and Event Reconstruction at PANDA

PANDA (Antiproton Annihilation at Darmstadt) [1] is a multi-purpose detector (see figure 1) currently under construction at the future Facility for Antiproton and Ion Research (FAIR) in Darmstadt, Germany. The fixed-target experiment aims to investigate a wide range of antiproton induced reactions with an angular coverage of nearly 4π. Amongst the many processes PANDA will study, there are those with challenging topologies involving secondary vertices. Considering that in many algorithms the interaction point often serves as a powerful constraint, reconstructing tracks that originate several centimetres or even metres away from the primary vertex can drastically increase the complexity of the algorithms. Matching track candidates with possible vertices may increase the computing requirements dramatically. Hyperon reactions prove to be good benchmark channels due to their complex topologies. These include displaced vertices, intersecting tracks, and final state particles which reach all of PANDA’s subdetectors for charged particle detection. The following studies put a particular focus on the pp → ΛΛ reaction and the Straw Tube Tracker (STT) subdetector

Pattern Matching
Neural networks
Findings
Conclusions
Full Text
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