Zooming in on the circumgalactic medium with GIBLE: Cloud-scale simulations with cosmological initial conditions

Abstract

We conduct simulations of $\sim$kpc-scale cool clouds in the circumgalactic medium (CGM), using initial conditions sampled from a highly resolved cosmological magneto-hydrodynamical zoom-in of a Milky Way-like galaxy. We select ten distinct cold clouds with masses of $m_{\rm{cloud}}$ $\sim$ $10^{4.5-5}$ M$_\odot$, originally resolved at a mass resolution of $m_{\rm{gas}}$ $\sim$ $200$ M$_\odot$. To further resolve small-scale features and physics, we implement a targeted refinement scheme within spherical regions co-moving with each cloud, thereby boosting the local mass resolution by a factor of 1000, reaching $m_{\rm{gas}}$ $\sim$ $0.2$ M$_\odot$ (spatial resolution, $r_{\rm{gas,cloud}}$ $\sim$ $O(\rm{pc})$). The selected clouds have diverse properties, across a broad parameter space, resulting in heterogeneous evolution. For the clouds we study, radiative cooling is the dominant physical process enabling cloud survival, while magnetic fields play a comparatively smaller role. The motion of these clouds is governed not only by drag forces that decelerate them, but also by acceleration from momentum exchange with the complex background velocity field, which can cause them to move faster than ballistic projectiles set by their initial velocities. Our results suggest that the non-trivial details of realistic cosmological initial conditions -- specifically the complex density, temperature, and velocity fields -- may play an important role in subsequent cloud evolution, and that sampling the output of an existing large-scale simulation provides a self-consistent approach to capture these effects without ad hoc assumptions.

Figures (10)

• Figure 1: An exemplary visualization of the technique explored in this work. The top panel shows a halo-scale projection of gas temperature, with the image stretching $\pm$ R$_{\rm{200c}}$ along the $x$ and $z$ axes, and R$_{\rm{200c}}$ along the $y$ axis. The white semi-circle is drawn at R$_{\rm{200c}}$, and the smaller circles at the positions of all clouds above a mass threshold of M$_{\rm{cl}}$$\gtrsim$$10^5$ M$_\odot$, with the radii of the circles scaled with the size of clouds. The cuboid represents a sample region (not to scale), centered on a cloud of radius R$_{\rm{cl}}$, extracted to be used as an initial condition for the cloud-scale simulation. The center panel shows a thin $\pm$ R$_{\rm{cl}}$ projection of this volume. The chosen cloud (#4; see Table \ref{['table:cloud_props']}) is highlighted by the dotted black box, and a zoomed-in projection is shown in the lower-left panel. The black dots in the center panel are drawn at the positions of the main descendants at future times (with a small horizontal offset to ensure they do not overlap), and the corresponding insets in the lower panel show analogous temperature-projections. While most of the volume in the extracted region is simulated at a mass resolution of $\sim$$200$ M$_\odot$ (center panel), a targeted spherical refinement region co-moving with the cloud boosts the local resolution to $\sim$$0.2$ M$_\odot$ (as seen in the lower panels). The elongated side of the cuboid is aligned with the cloud’s initial direction of motion, and all lower panels are rotated so that the cloud’s instantaneous mean velocity points to the right. This example shows how the method enables setting cloud-scale initial conditions while retaining the complexity of the surrounding CGM.
• Figure 2: A visualization of the small-scale velocity and magnetic fields around the same cloud shown in Figure \ref{['fig:mainIntroVis']}. From left to right, panels show thin $\pm$ R$_{\rm{cl}}$ projections of the magnitudes of the magnetic field, the velocity, and their respective curvatures. All panels are oriented so that the cloud’s mean velocity points to the right. Both the magnetic and velocity fields exhibit substantial structure on these scales: their magnitudes vary throughout the cloud and background, and the field directions are complex and non-uniform.
• Figure 3: A gallery visualizing the initial state of the ten clouds selected for our sample, with all oriented so that their mean velocity vector points to the right. Colors indicate the mean gas density in a thin projection, and scale bars in all panels correspond to $1$ kpc. Each cloud is unique in its initial morphology and exhibits distinct internal density distributions. Key properties of these clouds are summarized in Table \ref{['table:cloud_props']}. This figure highlights the diversity of initial conditions captured by our selection, which underlies the variety of subsequent cloud evolution.
• Figure 4: Individual growth profiles for the clouds in our sample. Each panel corresponds to a different cloud, arranged in the same order as Figure \ref{['fig:ic_gallery']}. Solid curves show the mass of the main descendant -- the single most massive cloud -- over time, while dashed curves include all descendants, accounting for fragments separated from the original object. Vertical dashed gray lines indicate the median cooling time in the cloud interfaces at the initial conditions. In all cases, this timescale is comparable to or shorter than the cloud crushing timescale (t$_{\rm{cc}}$), indicating that clouds are generally expected to grow steadily in mass. While some clouds follow this expectation, others show more complex behavior: some initially grow before losing mass, while a few undergo major mergers, producing sharp jumps in their masses. For comparison, dotted curves show the mass evolution in a 'pure hydrodynamic' simulation, i.e. without the physics of cooling or magnetic fields. In most cases, the mass decays steadily on timescales comparable to t$_{\rm{cc}}$, although abrupt spikes reveal instances where clouds 'revive' through mergers with fragments. Interestingly, clouds that merge in the pure hydro case do not always do so in the fiducial case, and vice versa. Overall, the figure illustrates the diversity of cloud growth trajectories and underscores the key role of additional physics in sustaining cloud mass over time.
• Figure 5: Examining the connection between the differential mass growth of clouds and the cooling luminosity of their ambient gas. To avoid overcrowding the panels, we randomly select five clouds, each shown with a unique combination of marker shape and color. The y-axis gives the mass-growth rate of the main descendant between consecutive snapshots, with snapshot time encoded by the marker edge color, as indicated by the colorbar. In the left (right) panel, the x-axis shows the cooling luminosity of gas in the interface (background) layer surrounding the clouds, $\mathcal{L}_{\rm{cool,int}}$ ($\mathcal{L}_{\rm{cool,bck}}$), evaluated at the snapshot corresponding to each point. The horizontal gray dotted line at zero separates net mass growth from net mass loss, the gray dashed curves show the best-fit lines for the stacked cloud sample, and the Spearman correlation co-efficient, r$_{\rm{s}}$, for the stacked point set is shown on the bottom left. At the population level, the differential mass growth increases towards larger values of $\mathcal{L}_{\rm{cool,int}}$, while the trend flattens out in the background.