Galaxy Modelling using Bayesian Statistics

Modelling disc galaxies is a notoriously difficult problem, partly because of the complexity of astrophysical effects that impact galaxy structure, and partly because the available data are often inadequate to properly constrain the model parameters. This book brings a Bayesian/Markov chain Monte Carlo approach to the problem, using the isolated dwarf spiral galaxy NGC 6503 as a test case. A comprehensive set of observations are available for fitting with sophisticated dynamical models. The joint posterior probability function for the model parameters is obtained, and hence constraints on such important properties as the galaxy mass and mass-to-light ratio, halo density profile, and structural parameters. This work should be useful to anyone interested in the properties of galaxies, as well as anyone with an interest in Bayesian techniques.

Autorentext

David Puglielli, Ph.D. in theoretical astrophysics, Queen''s University (Kingston) 2009, B.S. in Mathematics and Physics from McGill University (Montreal) 2002. Main research interests include galaxy dynamics, evolution and formation, numerical simulations, Bayesian techniques and their applications to astrophysical problems.