Particle detection optimization by means of a distributed evolutionary algorithm

This paper presents an approach to optimize the reconstruction of particle tracks in a large area spectrometer using an evolutionary algorithm. First, the basic concepts of the measurement of the momentum are presented, including some details on the track reconstruction. An objective function is formulated to measure the efficiency of the reconstruction. Then, the optimizer is described in two versions: sequential for a single CPU and distributed for a cluster of PCs. The architecture of both of them is presented in detail, including the description of the operators used plus some words on the infrastructure required for the distributed version. Finally, the results from the tests of the program are presented, from which the conclusion is drawn that the distributed genetic algorithm, easy to implement on high energy physics data processing clusters, may be helpful in improving the efficiency of the reconstruction.