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Ellert.van.der Velden

After having obtained my BSc in Physics and Astronomy and my MSc in Particle & Astrophysics (both at the Radboud University, Nijmegen, The Netherlands), I am up for the next challenge. At the age of 6, I discovered that due to my autism, I have the "inability" to understand and cope with the chaos of the world around me. I would love nothing more than surprises and changes becoming non-existent (good or bad ones). Because of this, I developed a huge interest in astrophysics at this age: A universe full of chaos, surprises and mysteries, that can be transformed into a describable system by using mathematics, physics and computational algorithms. And that leads me here.

Understanding the events during the early universe is still one of the greatest cosmological mysteries that we are facing. In order to try to understand these events and how they led to the universe we know today, we usually create semi-analytic models. Using semi-analytic models allows us to investigate what happened during the early universe by using the physical knowledge and observations we have today. However, a shared problem with all models is that they usually have a large number of parameters, which combined make up a decently large parameter space. Only a very small part of this parameter space can potentially create a model realization that would be interesting to look at. One of the most commonly proposed solutions is the usage of MCMC methods, but although they are quite reliable, they can be incredibly slow if evaluating the model takes some time. For that reason, I am developing an algorithm (based on the Bower et al. 2010 paper) that instead tries to construct an approximation of the model (Meraxes) by only using polynomial terms. Such a model is much faster to evaluate than the model it is based on and allows one to search for interesting parts of parameter space much quicker.

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