Simulating High-Velocity Clouds in the Observational Plane: An Initial Study with the Smith Cloud

TL;DR

This study compares simulations of the Smith Cloud with observations to understand how high-velocity clouds survive in the Milky Way's hot halo, highlighting the importance of turbulent radiative mixing layers.

Contribution

It introduces a detailed comparison between MHD simulations and observations of the Smith Cloud, emphasizing the role of turbulent radiative mixing layers in cloud survival.

Findings

Simulations can replicate some observational features like column density and velocity dispersion correlation.

Large-scale autocovariance of column density remains difficult to match in simulations.

The cloud with turbulent radiative mixing layer growth best matches observed properties.

Abstract

High-velocity clouds (HVCs) may fuel future star formation in the Milky Way, but they must first survive their passage through the hot halo. While recent work has improved our understanding of the survival criterion for cloud-wind interactions, few observational comparisons exist that test this criterion. We therefore present an initial comparison of simulations with the Smith Cloud (SC; $d=$ 12.4 kpc, $l, b = 40^{\circ}, -13^{\circ}$) as mapped with the GALFA-HI survey. We use the Smith Cloud's observed properties to motivate simulations of comparable clouds in wind tunnel simulations with Enzo-E, an MHD code. For both observations and simulations, we generate moment maps, characterize turbulence through a projected first-order velocity structure function (VSF), and do the same for HI column density with a normalized autocovariance function. We explore how initial cloud conditions (such as radius, metallicity, thermal pressure, viewing angle, and distance) affect these statistics, demonstrating that the small-scale VSF is sensitive to cloud turbulence while large scales depend on cloud bulk velocity and viewing angle. We find that some simulations reproduce key observational features (particularly the correlation between column density and velocity dispersion) but none match all observational probes at the same time (the large scales of the column density autocovariance is particularly challenging). We find that the simulated cloud (cloud C) showing growth via a turbulent radiative mixing layer (TRML) is the best match, implying the importance of TRML-mediated cooling for Milky Way HVCs. We conclude by suggesting improvements for simulations to better match observed HVCs.