How to Grow a Giant Galaxy



Title: MOSEL Survey: Tracking the Growth of Massive Galaxies at 2 < z < 4 using Kinematics and the IllustrisTNG Simulation
Authors: Anshu Gupta et al.
First Author’s Institution: University of New South Wales, Australia
Status: Published in ApJ

How, exactly, galaxies form is still very much an open question in astrophysics. It’s not like we can watch a galaxy evolve — most are about 12 billion years old, and even the youngest we’ve discovered is about 500,000 million years old.

There are two ways to work around this problem. The first is a simple matter of looking back into time. Light takes a finite amount of time to travel to us, and so the farther away we look, the older that light is. That means that the farther a galaxy is, the younger we see it. Instead of watching a single galaxy evolve over time, we can compare farther (“younger”) galaxies to closer (“older”) galaxies, and interpolate what may have happened to cause any changes.

The second way to work around our observational conundrum is to watch galaxies evolve in simulation space. The authors of today’s paper used IllustrisTNG100, part of a suite of large cosmological simulations of galaxy evolution. The cover image above shows a subset of luminous matter in the TNG100 simulation.

Observed Mass, Movement and Star Formation History

The kinematic properties (how things are moving) of star-forming galaxies is strongly linked to how they gained their mass. Today’s authors compared the velocity dispersion of observed “younger” galaxies at redshift z = 3.0–3.8 to “older” galaxies from previous studies of redshift ~ 2 and found that their most massive galaxies had smaller velocity dispersions than massive “older” galaxies.

velocity dispersion

Figure 1: Velocity dispersion as a function of mass, shown on a log–log scale. The authors’ “younger” galaxies are shown as gold stars. Other shapes represent previous studies of “older” galaxies at z ~ 2. The more massive galaxies in the authors’ sample are represented by larger stars and have smaller velocity dispersions than “older” galaxies of the same mass (shown in the red circle). [Adapted from Gupta et al. 2020]

By looking at the spectra of these galaxies, the authors could also extract their star formation histories. Basically, this looks at how old current stars are to extract the star formation rate over time. The top panels of Figure 2 show the authors’ results (keep in mind, time reads as newer on the left and older on the right). The bottom two panels show results from previous studies of galaxies at ~ 2. While the less massive galaxies in the authors’ survey (top left panel) show the same pattern of increasing star formation rate, the most massive galaxies on the right have relatively flat star formation histories. This is in contrast to massive galaxies at ~ 2, which show an increasing star formation rate over time.

star formation histories

Figure 2: Star formation histories for four different populations of galaxies. The x-axis is galaxy time before observation and the y-axis is star formation rate. The top two panels are the galaxies from the authors’ survey at z ~ 3 and the bottom two are from a previous survey at z ~ 2. The less massive galaxies are on the left and the more massive galaxies are on the right. The shaded areas indicate errors and the red arrows point toward trends. [Gupta et al. 2020]

Both the odd star formation histories and velocity dispersions point to something happening between = 3 and = 2 that changed massive galaxies. To determine what that might be, the authors turn to simulations.

Into the Simulation

The IllustrisTNG100 simulation starts with a distribution of mass at a redshift of = 127 and runs until present day, = 0. As it runs, the random fluctuations in density at = 127 turn into galaxies, which grow, form stars and merge. The authors wanted to look at how these galaxies acquired their stars over time.

There are basically two ways that a galaxy can gain stars: either by forming them from gas belonging to the galaxy (in situ) or by accreting the stars from other, mostly smaller, galaxies (ex situ). Figure 3 shows the fraction of stellar mass in the simulation that was accreted ex situ, rather than formed in the galaxy. It shows that for the most massive galaxies (in red), the fraction of ex situ stellar mass increases between = 3 (pink dotted line) and = 2 (black dotted line). Meanwhile, the ex situ stellar mass fraction remained largely constant for less massive galaxies (blue).

ex situ stellar mass fraction

Figure 3: The fraction of a galaxy’s stellar mass that was obtained from other galaxies, rather than formed in situ. Massive galaxies are shown in red, while less massive galaxies are shown in blue. The salmon and blue shaded regions are, respectively the error for the more massive and least massive galaxies. The black and pink dotted lines indicate, respectively, z = 2 and z = 3. [Gupta et al. 2020]

Uniting Simulations and Observations

The authors speculate that this increase in ex situ stellar mass fraction seen in simulations may be responsible for the increase in velocity dispersion seen in observed massive galaxies between = 3 and = 2. Turbulence and gravitational instabilities driven by accretion of stars and gas would increase the randomness of velocities (i.e. the velocity dispersion).

This could also explain the difference in star formation history between massive galaxies at = 2 and = 3 (Figure 2). Gas is necessary for the formation of stars and if the galaxies at = 2 have been able to gain gas from accretion, they would be able to increase their star formation rate, as seen in the bottom right panel of Figure 2. In contrast, a smaller ex situ stellar mass fraction for = 3 galaxies indicates that there has been less accretion and less opportunity to gain new gas and thus form new stars, leading to the flat star formation trend seen in the top right panel of Figure 2.

Essentially, the younger galaxies at = 3 have had less time to merge with other galaxies, leading to smaller velocity dispersions and less star formation.

The authors note that their conclusions are limited by many factors, including a small sample size. However, these are promising results and show how much can be gained by comparing observations and simulations.

About the author, Bryanne McDonough:

Graduate student at Boston University where I am currently studying the distribution of dark matter and satellites around galaxies using data from the Illustris simulations. My primary research interests are galactic and extragalactic astrophysics using computational methods. Outside of grad school I enjoy reading and crafting.