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