It’s an interesting idea to use the secrets of the universe to keep your own secrets.
This month I’ve upgraded my home computer. My new desktop has faster processor, double the storage space, and quadruple the RAM as my venerable old laptop. I don’t upgrade very often, so when it happens there’s a very noticeable uptick in computing power. It’s something we’ve become rather accustomed to. With each new phone, computer or tablet we have more power at our fingertips. This consequence of Moore’s law has also revolutionized the way we do astronomy.
One of the common ways we can map the distribution of matter in a galaxy is by observing the light emitted neutral hydrogen. This works pretty well because hydrogen is the most abundant element in the universe, and its emission lines are pretty distinctive. But for distant galaxies hydrogen emissions aren’t very bright. To observe them you need really long exposure times, and that limits the amount of galaxies you can observe. One alternative is to look at the emissions of carbon instead.
One of the challenges faced by astrophysicists is that you can’t repeat your experiments. With cosmology, that poses a particular challenge because we only have one observable universe. Not only can’t we repeat the experiment, we only have one experiment to observe. What we can do, however, is simulate the universe and see how it compares to the real one.
As computers have grown ever more powerful, astronomers and astrophysicists have increasingly used computers to model the complex systems they study. This can range from modelling the motions of planetary bodies in our solar system, to simulating the convection of plasma in the depth of a star. Perhaps the most ambitious computer modeling project, however, is the Millenium Project at the Max Planck Institute.
How do you deal with chaos in computational astrophysics? It turns out there are ways to analyze the properties of a solution even if you don’t know what the exact solution is.
When you do computational astrophysics, you can get errors in your results. The trick is to recognize where those errors lie, and to learn to minimize them.
We revisit the simple model of a star to see how to make a better stellar model.