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Question: What are you trying to learn from running simulations and how can you prove your theories?
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Asked by anon-252841 to Miriam on 5 May 2020.Question: What are you trying to learn from running simulations and how can you prove your theories?
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Sarah commented on :
I study galaxy evolution in simulations, so that means trying to answer questions about how the galaxies in the real universe ended up the way they are!
One of the issues in astronomy is that things often take millions or billions of years to evolve and change, especially when we look at large or distant objects. Because they take so long to do anything, we can’t watch their evolution over a human lifetime. Normally in science we can do experiments and wait for them to finish and then we can see the results, but we don’t get to do that often in astronomy, unless we’re looking at really nearby objects or super extreme fast events. That’s why we run simulations, so that we can try to understand how the objects we see in space came to be the way they are.
The best way to test a simulation is to compare it to the real thing! When we compare to observations from the real universe, such as real galaxy shapes and sizes, it tells us what we should expect our simulations to look like. In the simulations I use, it’s often really simple things like, how many of each type of galaxy should we see? How many massive galaxies, low mass galaxies, spiral galaxies etc, are in the real universe? This is what we’d like to see in the simulations. If the simulation looks nothing like the real universe then it means we got something wrong and we need to go back to the drawing board.
Another issue in astronomy is that often when we observe space we often don’t have access to all the measurements we would like to make. For example, it’s great to try to measure as much of the electromagnetic spectrum as possible, because different wavelengths of light arise in different conditions. But often we only have access to one or two wavelengths, so we don’t get to investigate all the conditions we’d like to. That’s why simulations are great – we can fill in some of these knowledge gaps because we know everything about the objects in our simulations.