Poster abstract details
Bayesian Inference to Analyze the Global Stability of Galaxies
Abstract
Bayesian evidence is being increasingly important in Astrophysics. The dynamics of the galaxies can be explained either by introducing a missing mass component (dark matter) and the Newton's law of gravitation or by assuming a non-linear modification of Newton's law of gravitation (MOND).
In this work we perform a Bayesian analysis to estimate and compare the results of both approaches using data from the rotation curve of a sample of galaxies.
For each one the astrophysical models involved are highly nonlinear and the number of observations is small, reason why Bayesian inference is the preferred approach to address this issue.
In this work we perform a Bayesian analysis to estimate and compare the results of both approaches using data from the rotation curve of a sample of galaxies.
For each one the astrophysical models involved are highly nonlinear and the number of observations is small, reason why Bayesian inference is the preferred approach to address this issue.