A Symplectic Method to Generate Multivariate Normal Distributions

• Main Author: Baumgarten, Christian
• Format: Journal Article
• Language: English
• Published: 16.05.2012
• Subjects: Mathematics - Mathematical Physics; Physics - Data Analysis, Statistics and Probability; Physics - Mathematical Physics
• DOI: 10.48550/arxiv.1205.3601

The AMAS group at the Paul Scherrer Institute developed an object oriented library for high performance simulation of high intensity ion beam transport with space charge. Such particle-in-cell (PIC) simulations require a method to generate multivariate particle distributions as starting conditions. In a preceeding publications it has been shown that the generators of symplectic transformations in two dimensions are a subset of the real Dirac matrices (RDMs) and that few symplectic transformations are required to transform a quadratic Hamiltonian into diagonal form. Here we argue that the use of RDMs is well suited for the generation of multivariate normal distributions with arbitrary covariances. A direct and simple argument supporting this claim is that this is the "natural" way how such distributions are formed. The transport of charged particle beams may serve as an example: An uncorrelated gaussian distribution of particles starting at some initial position of the accelerator is subject to linear deformations when passing through various beamline elements. These deformations can be described by symplectic transformations. Hence, if it is possible to derive the symplectic transformations that bring up these covariances, it is also possible to produce arbitrary multivariate normal distributions without Cholesky decomposition. The method allows the use of arbitrary uncoupled distributions. The functional form of the coupled multivariate distributions however depends in the general case on the type of the used random number generator. Only gaussian generators always yield gaussian multivariate distributions.