Time-Based Reconstruction of Hyperons at PANDA at FAIR

Abstract: The PANDA (anti-Proton ANnihilation at DArmstadt) experiment at the future FAIR (Facility for Anti-proton and Ion Research) offers unique possibilities for performing hyperon physics.The almost full 4π coverage of the detector will enable the reconstruction of both hyperon and antihyperon which will be created together in proton-antiproton collisions. This enables investigations of the strong interaction in the non-perturbative regime. The relevant degrees of freedom for hyperon production is not yet known here and the self analyzing wek decay ofhyperons enables the measurement of spin observables that could discriminate between different production models. Hyperons are generally identified through their decay products. Due to their relatively long-lived nature, the displaced decay vertices of the hyperons impose a particular challenge on the track reconstruction and event building. PANDA will utilize a fully software-based event filtering. Track reconstruction will be crucial for the online filtering where it willbe used together with the event building. Therefore, track reconstruction that works well for particles created a measurable distance away from the interaction point and can work on free streaming data is therefore crucial for the reconstruction of hyperons.In this thesis, investigations of the detector signatures from the decay channels Λ→pπ-, Ξ-→Λπ- and Ω-→ΛK- are presented. The detector signatures guide the subsequent track reconstruction algorithms. A candidate for online track reconstruction algorithms on free streaming data based on a 4D Cellular Automaton clustering procedure has been developed and is reported on. The Cellular Automaton procedure utilizes information from the PANDA straw tube tracker. It does not assume the interaction point as a constraint which makes it suitable for tracking hyperon decay products. The track reconstruction quality assurance procedure and the results from the tracking at different event rates are also presented. The 3D version of the algorithm, where only spatial information is utilized, is compared to the 4D version. Results show that utilizing the time-stamp information in the clusterization procedure increases the tracking efficiency at higher interaction rates. In addition, the fake rate of tracks is suppressed at high erevent rates with the 4D Cellular Automaton. Finally, extrapolation algorithms for including hit information from additional detectors in the tracks reconstructed in the straw tube tracker of PANDA are outlined.