A New Open Code for SED Fitting of Far-IR Images Using a Hierarchical Bayesian Algorithm
Monday, 16 February 2015 1:30 p.m. — 2 p.m. MST
AURA Lecture Hall
Far-IR emission can provide a lot of information about dust properties of different astronomical objects. By fitting the Spectral Energy Distribution (SED) we can derive information about the temperature, chemistry and density of the dust. We are following the scheme developed by Kelly et al. (2012) which uses a new fitting approach based on a Hierarchical Bayesian analysis. This method shows promising results compared to the classical Maximum Likelihood, which sometimes shows a false anti-correlation between some of the parameters. In this project we are comparing these approaches on Herschel data of Planetary Nebulae, and we are building an open code that uses the Hierarchical Bayesian method to reliably derive the physical parameters of the dust. This code can be used and improved by anyone in the astronomical community.