Feeding CAMELS: An Exploration of Galactic Feedback Effects in Simulations

Title:  Quantifying Baryonic Feedback on Warm-Hot Circumgalactic Medium in CAMELS Simulations

Authors: Isabel Medlock, Chloe Neufeld, Daisuke Nagai, Daniel Anglés Alcázar, Shy Genel, Benjamin Oppenheimer, Priyanka Singh, Francisco Villaescusa-Navarro

First Author’s Institution: Department of Astronomy, Yale University, New Haven, Connecticut, USA

Status: Submitted to arXiv (preprint) and the Astrophysical Journal (open access)

Simulating the Universe

Galaxies are complex ecosystems with a wide variety of physical processes impacting how they form and evolve. Studying these processes  is often quite difficult, in part because we can’t actually travel to galaxies and poke and prod at them to see what’s going on. So we have to turn to another handy tool in the astrophysicist’s toolkit: simulations. Cosmological hydrodynamic simulations can model how giant swaths of the universe have evolved since its early years, and take into account a wide range of processes ranging from dark matter structure formation to star formation to galaxy mergers. But because these simulations are so large and complex, there is a limit to how small of a scale can be resolved, and a lot of the activity falls into what we call “subgrid physics” – phenomena that occur on scales below a simulation’s resolution. Instead of modeling activity from foundational behavior, we have to approximate what impact they would have on the scales we can resolve. For example, instead of directly modeling how a molecular cloud collapses, we would say if a volume of gas reaches some critical density it automatically forms some amount of stars with some initial mass function. (If that sounds a bit hand-wavy, that’s because it is, but we gotta work with what we’ve got and right now we don’t have the computing power to model all the scales of astrophysics at once.) 

Some of the most important subgrid processes are stellar and black hole feedback. Stellar feedback refers to how star-related events like star formation and supernovae deposit energy into the interstellar medium, which can impact the galactic environment significantly, slowing down star formation and even ejecting material from the galaxy entirely. Black hole feedback (sometimes called active galactic nuclei or AGN feedback) comes from activity around the supermassive black holes found in the center of practically every large galaxy. As material acretes around the black hole, it can heat up or even be ejected at high velocities, causing large amounts of energy to be expelled in and around the host galaxy. Between stars and black holes, these feedback processes have a large impact, and without their inclusion in simulations the results look nothing like the universe as we observe it.

A Flock of Camels

There are many cosmological simulations widely used today, but comparing their differing feedback implementations can be difficult as other parameters (i.e. initial conditions, cosmological parameters, resolution and simulation volume) are varied. Thus the simulations used in this work come from the CAMELS project, which provides a suite of runs that change feedback parameters but keep everything else constant. CAMELS pulls feedback implementations from several simulations, but this work looks specifically at comparing the simulations SIMBA and IllustrisTNG. For each of these simulations, CAMELS identifies four parameters that drive feedback (two each for stellar and blackhole processes), and provides a range of runs that span this parameter space. These parameters describe things like the mass loading and speed of winds, accretion rates, and energy and momentum flux. 

SIMBA and IllustrisTNG are broadly similar in their feedback treatments, but have a few key differences. To model stellar feedback, both codes heat and move around gas particles, but the means in which they calculate temperature gains and velocities are different. Both simulations have two modes of AGN feedback, one associated with low accretion rates around the black hole, and the other associated with high accretion rates. For IllustrisTNG, when accretion rates are high the feedback is all thermal, heating nearby gas particles. At lower accretion rates, the feedback is more kinetic, where particles are ejected with energies set by the CAMELS blackhole feedback parameters. In SIMBA, both AGN modes have some form of kinetic feedback, with high accretion rates corresponding to lower velocities. 

Figure 1:  Relationship between the halo mass and closure radius in different simulations ran with varying stellar feedback strengths. Here, we can see the ability of SIMBA (purple) to disperse gas to a larger radius than IllustrisTNG (green). Adapted from Figure 4 in the paper.

This work compares how the different feedback treatments in CAMELS-SIMBA and CAMELS-IllustrisTNG impacts black hole growth, feedback energies, and gas distribution in and around galaxies. The authors do this by comparing two different values: fCGM and the closure radius. fCGM  provides a ratio of the mass found in the circumgalactic medium (the region surrounding a galaxy) relative to the total mass found in the halo. The closure radius takes a different approach, by looking at the physical scale at which the ratio between the mass of baryons (gas, stars, and black holes) and all the mass (including dark matter) is the same as the ratio for the universe as a whole. The measurement provides a way of determining the scale at which matter is associated with a given galaxy.   

Some Feedback on the Feedback

Overall, this work found that the variations in feedback processes not only impacted the gas distribution and galaxy properties, but also that the links between stellar and AGN feedback are important and vary between different simulations. In general, the feedback implemented by the IllustrisTNG code had a higher energy than that from SIMBA. However, SIMBA had a greater impact on the baryon distribution, with larger closure radii than the IllustrisTNG runs, as shown in Figure 1. When examining links between stellar and blackhole feedback, it was found that improving the efficiencies of stellar feedback weakened AGN feedback in IllustrisTNG, but slightly strengthened it in SIMBA. Figure 2 shows how varying one of the stellar feedback parameters changed the energy of an AGN feedback mode. Finally, they looked at changes over time; earlier in the universe AGN feedback was more rare than stellar activity, and the complexities of their interplays did not become important until redshifts < 2. 

Figure 2:   Relationship between the halo mass and energy coming from the thermal AGN mode in IllustrisTNG (left) and SIMBA (right). The different colors represent different strengths of stellar feedback, with purple being the lowest and orange the highest. Here we see how changing the stellar feedback parameters impacts the blackhole feedback, with stronger stellar feedback corresponding to lower AGN energies in Illustris TNG, but slightly higher energies in SIMBA. Adapted from Figure 1 in the paper.

Overall, these results indicate how one cannot simply treat stellar and blackhole feedback independently (insert Boromir meme here). These processes are related in complex ways that are currently not fully understood. The fact that different simulations have different interplays (sometimes with directly opposite results!), points to a need for further constraining of these subgrid models going forward.

Astrobite edited by Lucie Rowland

Featured image credit: NASA, ESA and the Hubble Heritage Team (STScI/AURA), S. Grayson 

About Skylar Grayson

Skylar Grayson is an Astrophysics PhD Candidate and NSF Graduate Research Fellow at Arizona State University. Her primary research focuses on AGN feedback processes in cosmological simulations. She also works in astronomy education research, studying online learners in both undergraduate and free-choice environments. In her free time, Skylar keeps herself busy doing science communication on social media, playing drums and guitar, and crocheting!

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