Cloud-by-cloud, multiphase, Bayesian modeling: Application to four weak, low ionization absorbers

Abstract

We present a new method aimed at improving the efficiency of component by component ionization modeling of intervening quasar absorption line systems. We carry out cloud-by-cloud, multiphase modeling making use of CLOUDY and Bayesian methods to extract physical properties from an ensemble of absorption profiles. Here, as a demonstration of method, we focus on four weak, low ionization absorbers at low redshift, because they are multi-phase but relatively simple to constrain. We place errors on the inferred metallicities and ionization parameters for individual clouds, and show that the values differ from component to component across the absorption profile. Our method requires user input on the number of phases and relies on an optimized transition for each phase, one observed with high resolution and signal-to-noise. The measured Doppler parameter of the optimized transition provides a constraint on the Doppler parameter of HI, thus providing leverage in metallicity measurements even when hydrogen lines are saturated. We present several tests of our methodology, demonstrating that we can recover the input parameters from simulated profiles. We also consider how our model results are affected by which radiative transitions are covered by observations (for example how many HI transitions) and by uncertainties in the b parameters of optimized transitions. We discuss the successes and limitations of the method, and consider its potential for large statistical studies. This improved methodology will help to establish direct connections between the diverse properties derived from characterizing the absorbers and the multiple physical processes at play in the circumgalactic medium.

Accepted for Publication in MNRAS

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