Multiphase modeling of Quasar Absorption Lines using Bayesian Inference

A major shortcoming in ionization analysis of Quasar absorption line systems is that absorbers are often characterized by a single value of metallicity and density obtained by grouping all the components together. We have developed a new method to rectify this problem that performs component-by-component, multiphase ionization modeling of absorption systems. I will be presenting our method that performs both photoionization and collisional ionization modeling of QSO absorption lines with CLOUDY and extracts their physical properties by using Bayesian inference. This method employs “Optimizing” ions to trace the different phases present in an absorption system. Temperature derived from the CLOUDY modeling of “optimized” ions is used to constrain the Doppler broadening parameter for hydrogen and other observed transitions seen in the spectra. I will illustrate the method for four weak low ionization absorbers and compare to results of traditional methods, and discuss the potential and limitations of the method.