Beyond radial profiles: Using log-normal distributions to model the multiphase circumgalactic medium

TL;DR

This paper introduces a log-normal distribution-based probabilistic model for the multiphase circumgalactic medium, capturing its broad temperature range and complex structure better than traditional density profiles, and constrains it with Milky Way-like galaxy data.

Contribution

It extends previous CGM models by incorporating 2D density-temperature PDFs using log-normal distributions, providing a more realistic and flexible framework for interpreting observations.

Findings

Model reproduces observed ion column densities and spectra.

Hot gas properties are well constrained by X-ray and radio data.

Provides a versatile analytic framework for multiphase CGM analysis.

Abstract

Recent observations and simulations reveal that the circumgalactic medium (CGM) surrounding galaxies is multiphase, with the gas temperatures spanning a wide range at most radii, $\sim 10^4\ {\rm K}$ to the virial temperature ($\sim 10^6$ K for Milky Way). Traditional CGM models using simple density profiles are inadequate at reproducing observations that indicate a broad temperature range. Alternatively, a model based on probability distribution functions (PDFs) with parameters motivated by simulations can better match multi-wavelength observations. In this work, we use log-normal distributions, commonly seen in the simulations of the multiphase interstellar and circumgalactic media, to model the multiphase CGM. We generalize the isothermal background model by Faerman et al. 2017 to include more general CGM profiles. We extend the existing probabilistic models from 1D-PDFs in temperature to 2D-PDFs in density-temperature phase space and constrain its parameters using a Milky Way-like {\tt Illustris TNG50-1} halo. We generate various synthetic observables such as column densities of different ions, UV/X-ray spectra, and dispersion and emission measures. X-ray and radio (Fast Radio Burst) observations mainly constrain the hot gas properties. However, interpreting cold/warm phase diagnostics is not straightforward since these phases are patchy, with inherent variability in intercepting these clouds along arbitrary lines of sight. We provide a tabulated comparison of model predictions with observations and plan to expand this into a comprehensive compilation of models and data. Our modeling provides a simple analytic framework that is useful for describing important aspects of the multiphase CGM.