Discrimination Between Different Types of Material in Track Reconstruction With a Gaussian-Sum Filter

In the context of track reconstruction, the description of the material in a detector is frequently a simplified version of the detailed description used in the simulation. In particular, complex structures of different types of materials are often approximated by simple layers consisting of a material with average properties. Multiple scattering is then usually modeled by a Gaussian distribution, and energy loss by a narrow Gaussian or a delta function (Dirac distribution). More detailed information about the material can be taken into account by modeling multiple scattering and energy loss by mixtures of Gaussians and Dirac distributions, respectively. Each component of the mixture corresponds to a particular type of material, and the weight of the component is given by the probability to encounter this type of material. Track fitting using these mixtures can then be accomplished by the Gaussian-sum filter (GSF). Its performance is compared to a standard Kalman filter using the average material properties. The gain in precision of the estimated track parameters is studied in a series of simulation experiments, and the most promising scenarios are identified