Presentation on theme: "Simulating Cosmological Radiative Transfer (or: How to Light Up The Universe) Introduction: Behind the term radiative transfer hides a theory that mathematically."— Presentation transcript:
Simulating Cosmological Radiative Transfer (or: How to Light Up The Universe) Introduction: Behind the term radiative transfer hides a theory that mathematically describes the propagation of light through matter. The development of radiative transfer is historically linked to the emergence of astrophysics in the first quarter of the 20th century, mainly the investigation of stellar evolution and structure. Indeed, it would be hard to imagine stars without starlight (that was before neutron stars and black holes). Modern theoretical astrophysics regularly utilizes computer simulations as a 'laboratory'. These simulations tend to need as much computational resources as they can get so the use of parallel processor supercomputers is a standard. Cosmology is especially demanding - number of particles in the biggest structure formation simulations is already reaching billions. Because of these high costs and some other more specific problems, the role of light in the formation simulations of of today's universe was largely ignored, but recent observational advances have put forth an immediate need for more complete simulations. That is why a number of new methods for numerical radiative transfer have been developed in recent years. Our work consists of developing one such method and applying it to the current burning issues in modern cosmology. A bit of theory: The theory of radiative transfer is not a simple matter at all but, fortunately for cosmologists, desired results can be obtained even with the use of a number of simplifying approximations. In our model we consider a point source of light, which is strong enough for us to ignore diffusion. We follow the propagation of photons by casting a number of rays and calculating how the intensity of light changes along these rays: where I(r) is the intensity at distance r, I(0) the intensity at the source and τ is the optical depth: where a 0 is the cross-section and N(r) is the column density (the density 'along' the ray). Hydrodynamical Adaptive Mesh Refinement method: In the Eulerian hydrodynamical scheme, the fluid is described by a grid of cells, where every cell represents a part of the observed area with the one value of variables such as density, pressure, temperature and so on. To precisely represent the physical world, that grid should be infinitely fine, but in practice, that is obviously impossible. We are limited by our computing power so we always have a finite sized cells of the grid. Yet certain problems need to have a very fine resolution in order to resolve the interesting structures and phenomena. That is where AMR steps in. The main idea of this method is to have a different cell size in certain parts of the grid, meaning to increase the resolution around interesting phenomena and to decrease it in the other parts. Example of a partially resolved grid divided between two processors can be seen on picture 4. and AMR in action can be seen in a picture 8. at the bottom right of the poster. In our work, we use a publicly available code, FLASH, developed in the Alliance Center For Astrophysical Thermonuclear flashes at the University of Chicago. FLASH is a fully parallel AMR code well suited for many different hydrodynamical problems. It is written in Fortran 90 and uses its modular structure to the maximum which makes additions to the code to be quite simple and straightforward. Where to go from here: As mentioned at the beginning, radiative transfer is essential in a large number of astrophysical problems. What we are interested in, are its applications in cosmology. In the general picture of structure formation, radiation comes in rather late – obviously not before the formation of the first sources of light. Still, once those sources are formed radiative transfer becomes indispensable for understanding the further evolution of the universe. Thanks to the quasar absorption lines data, it is a well known fact that the universe was mostly neutral up to a certain point and then it was rapidly ionized and got to a state of very high ionization in which we observe it today. That period is called reionization and it is closely linked to the emergence of the first light sources in the universe. Yet, a part from a rough estimate of when reionization happened, not much more is known. There are still a lot of open questions, such as what exactly were the sources of reionization – quasars or the first stars formed in early galaxies? What effects did it have on the formation of structures in the universe, a process well described by only gravity up to the point of reionization. The interest in computational approach to these problems has been ignited in not more than the last 5 to 10 years, first because of the rise in available computing power needed to do radiative transfer along with gravity and hydrodynamics. Second, a number of new observational experiments, such as LOFAR that will measure the 21cm neutral hydrogen line to very high redshifts, promise to give us unprecedented insight in this period in the history of our universe. The next step: As a first project we have chosen to look at the evaporation of mini haloes during reionization. In theory, first light can inject enough heat into smaller dark matter haloes, effectively stopping them from forming galaxies. That effect could explain the discrepancy between the observed cutoff in the lower end of the galaxy distribution (the so called luminosity function) and the cold dark matter gravity-only simulations that predict a much larger number of small galaxies than it is being observed. Simulating this problem needs a code that is capable of handling gravity, hydrodynamics and radiative transfer all together which is what our code is made to do. We are hoping for the first runs to be complete by the end of this summer. Milan Raičević Institute for computational cosmology, Durham University Extending the method: The hybrid characteristics model is, of course, not perfect. One of its most serious problems is the lack of the, so called, photon conservation. That is a large issue in the whole radiative transfer field. In a nutshell, it is an error arising from numerical discretization, both of space and time. The effect is that the results for the positions of ionization fronts, the area of space that has been ionized up to a certain time, are not robust in regards to changes in grid resolution or time steps. It is not a big issue in every problem, but in cosmology, we need the speed of the fronts to be as accurate as possible and that is why we are working to correct the HC method. The issue is not trivial and requires more room to explain than we have here. To demonstrate the effects of photon conservation, we have included picture 5. Hybrid Characteristics: Hybrid characteristics (Rijkhorst et al. 2006) is a rather new method for transporting radiation, made for use on parallelized adaptive mesh refinement codes (AMR). In essence, it is a ray- tracing algorithm that combines two ray-tracing schemes widely used in astrophysics: the long and short characteristics. The most natural approach for ray-tracing is to draw a ray from the source of light to the cell which is being considered (picture 1). That method is called long characteristics. Unfortunately, as can be seen from the picture, long characteristics are quite computationally intensive, especially in the cells near the source of the rays where there are a lot of unnecessary calculations. To remedy this problem, the method of short characteristics was introduced. As seen on picture 2., instead of calculating contributions for every ray crossing a cell, we only calculate the values to the corners of the cells and from them interpolate the values for every individual ray. This method is much more computationally effective but it introduces new problems: first, it introduces a certain amount of numerical diffusion and second, since the ray is constructed through interpolation cell by cell going away from the source, it is very hard if not impossible to parallelize. Hybrid characteristics borrows from both of these approaches and combines them to make ray-tracing effective on a parallelized system. In short, on every part of the problem box belonging to a single processor, that part of the ray is being calculated with long characteristics, but the communication between different computational domains borrows the interpolation idea – only the values of the column density at the corners of the cells on the edge of every domain are communicated between processors. The final ray is constructed through summing up the contributions of every processor 'cut' by the ray. Picture 1. long characteristics method Picture 2. short characteristics method Picture 3. Interpolation scheme in Hybrid characteristics Picture 4. an AMR grid distributed between two processors Picture 5. Effects of photon conservation on position of the ionization front Picture 6. Expansion of ionized bubbles during reionization. Blue regions are ionized, while green are neutral (Mellema et al. 2006) Picture 7. Position-redshift slice that represents the large scale geometry of reionization as it should be seen by future 21-cm surveys such as LOFAR (Mellema et al. 2006) Picture 8. Density plot of evaporation of a dense spherical clump by a point source. Its an idealized version of mini halo evaporation Background: a snapshot of the Millennium simulation at z = 5.7 (Millennium was run by the Virgo Consortium)
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