In this work, the performance of a proposed algorithm to search for clusters in the PANDA electromagnetic calorimeter (EMC) in real time, which provides vital informationfor the online event selection procedure, is discussed. Two implementations will be discussed and compared to each other and to a third, less suited algorithm (at least, as far as online usage is concerned). After this, the implementation in the readout hardware and concepts for the following data collection network will be presented.

The non-perturbative nature of the strong interaction leads to spectacular phenomena, such as the formation of hadronic matter, color confinement, and the generation of the mass of visible matter. To get deeper insight into the underlying mechanisms remains one of the most challenging tasks within the field of subatomic physics. The antiProton ANnihilations at DArmstadt (${\bar{\rm P}}$ANDA) collaboration has the ambition to address key questions in this field by exploiting a cooled beam of antiprotons at the High-Energy Storage Ring (HESR) at the future Facility for Antiproton and Ion Research (FAIR) combined with a state-of-the-art and versatile detector. In this contribution, I will address some of the unique features of ${\bar{\rm P}}$ANDA that give rise to a promising physics program together with state-of-the-art technological developments.

PANDA is a planned experiment at FAIR (Darmstadt, Germany) with a cooled antiproton beam in a range [1.5; 15] GeV/c, allowing a wide physics program in nuclear and particle physics. It is the only experiment worldwide, which combines a solenoid field (B=2T) and a dipole field (B=2Tm) in an experiment with a fixed target topology, in that energy regime. The tracking system of \panda involves the presence of a high performance silicon vertex detector, a GEM detector, a Straw-Tubes central tracker, a forward tracking system, and a luminosity monitor. The offline tracking algorithm is developed within the PandaRoot framework, which is a part of the FAIRRoot project. The algorithm here presented is based on a tool containing the Kalman Filter equations and a deterministic annealing filter ($genfit2$). $genfit2$ offers to users also a Runge-Kutta track representation, and interfaces with Millepede II (useful for alignment) and RAVE (vertex finder). The Kalman-Filter-based algorithms have a wide range of applications; among those in particle physics they can perform extrapolations of track parameters and covariance matrices. The impact on physics simulations performed for the \panda experiment is showed for the first time, with the PandaRoot framework: improvement is shown for those channels where a good low momentum tracking is required ($p_T$ <350 MeV/c) of about a factor 2.