Cloud-by-cloud, multiphase, Bayesian modeling: Application to four weak, low ionization absorbers
Sameer, J. C. Charlton, J. M. Norris, M. Gebhardt, C. W. Churchill, G., G. Kacprzak, S. Muzahid, Anand Narayanan, N. M. Nielsen, Philipp Richter, and, Bart P. Wakker

TL;DR
This paper introduces a Bayesian, cloud-by-cloud multiphase modeling approach using CLOUDY to analyze quasar absorption lines, providing detailed physical properties of low ionization absorbers with improved accuracy.
Contribution
The paper presents a novel Bayesian method for component-wise ionization modeling of absorption systems, enhancing parameter inference and addressing limitations of previous approaches.
Findings
Successfully recovers input parameters from simulated profiles.
Demonstrates the method's ability to constrain metallicities and ionization parameters.
Analyzes effects of observational coverage and uncertainties on results.
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…
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