Research Interests

Galaxy Formation

One of the last pieces of confirming ΛCDM is taking the early Universe as we know it was from WMAP and evolving it using physics that makes sense into the galaxies that we see today. Stinson et al (2010)

Stellar Feedback

Early simulations of galaxy formation suffered from over-collapse. Governato et al (2004) explains how part of the problem is lack of resolution. The point of that paper is that two body scattering of gas particles off large dark matter preferentially removes angular momentum from the gas, so it falls into the center and makes a dense central concentration.

A second problem is that cooling goes as a function of density squared, so dense regions will collapse if they are not provided with pressure support. In the real Universe, stars provide this support to gas all over galaxies. However, in simulations, cooling times were shorter than the dynamical times of the dense gas, so energy ejected into the surrounding gas never had a chance to exert pressure support. Thus, when stars formed, they could keep on forming because the energy they release in stellar winds and supernovae was immediately lost. When I arrived at grad school, a n-body SPH solver, Gasoline, had been written, but it did not include any effective feedback effect from supernovae.

James Wadsley had worked on adiabatic feedback with Thacker and Couchmann (2000, 2001), so we put it into Gasoline. The initial recipe they used turned off cooling across all 32 particles that make up the smoothing kernel for 30 Myr. The original recipe from Gerritsen (1997) were great, but was similarly inflexible. It seems like stellar feedback will behave differently depending on the environment in which it occurs. So, I decided as a first shot to use a parameter to decide how many gas particles should have their cooling shut off. The details are in Stinson et al. (2006). That was fine, but why parameterize when McKee & Ostriker (1977) gives an explicit formula for the effect of supernovae blastwaves on the ISM? So, I put that physics in and eliminated the parameters that specified how many particles to disable cooling in for how long.

How Well It Works

• These papers all use the blastwave feedback to solve a whole range of the problems associated with galaxy formation.

I ran a variety of simple virialized halo model galaxies of different masses. The most interesting galaxies were the low mass dwarfs like the one at the left. The link plays a movie of how supernovae act as a significant feedback on star formation. Stars form, supernovae explode and stars cannot form any more until the gas cools back down and collapses.

• Sijing Shen published a paper paper about how the feedback works at populating the IGM with metals in a cosmological simulation.

New Development

• In addition to all the IDL required to do the analysis of these simulations, I've worked on a new more powerful analysis tool, NChilada that is parallel. It uses a new output format that takes advantage of directories. It puts different attributes into their own files and files them in directories according to time step and particle type. The viewer, Salsa has a java interface that allows you to view and manipulate the large datasets in real time (we wouldn't be able to view the billion particles any other way). Salsa also hooks into python so that you can manipulate all your data in python.

• I made sure that Gasoline writes output in the new format. Breaking data up in this way makes it possible to analyze the data more easily on desktop machines as you only have to read in one or two attribute files to get the data that you desire.

• Another exciting part of the whole NChilada is a brand new n-body gravity solver, Changa. It has been written by our collaborators in the CS department at UIUC. Using their parallel framework, Charm, it scales great on a commodity cluster. Thanks to the efforts of Fillippo Gioachin, Changa allows for domain decomposition in several different ways, uses a very efficient cache management scheme for communcation, and implements several varieties of tree. Once we implement SPH into the code, I expect to use it for my runs that include gas. For now, Gasoline is a fine option.

• I wrote documentation (password required, hopefully open sourced soon?) for pkdgrav and gasoline.

• The bread and butter of the simulation game is particle tracing. How did particles get to where they are today? (Or, in the real world, how did the elements that make up the earth get where they are today?) Quite often, it is interesting to figure out which halos got swallowed by which and which particles got blown out of which halos. You might call them "merger trees". I have written IDL and C functions to determine which end halos came from which halos in an earlier era.