There are basically two things astronomers need to do in order to learn
something about the universe. First, we need to observe something in
the sky, using a telescope and some sophisticated detection equipment.
Second, we need to interpret the observations by trying to match them
with a mathematical model, using a computer. The models have a bunch
of parameters - sort of like knobs and
switches that can be adjusted - and each represents some aspect of
the physical laws that govern the behavior of the model.
When we find a model that seems to match the observations fairly well,
we assume that the values of the parameters tell us something about
the true nature of the object we observed. The problem is: how do we
know that some other combination of parameters won't do just as well,
or even better, than the combination we found? Or what if the model
is simply inadequate to describe the true nature of the object?
The process of adjusting the parameters to find a `best fit' model to the
observations is essentially an optimization problem. There are many well
established mathematical tools (algorithms) for doing this - each with
strengths and weaknesses. I am using a relatively new approach that uses
a process analogous to Charles Darwin's idea of evolution through natural
selection. This so-called
genetic algorithm explores all of the many possible combinations of
parameters, and
finds the best
combination for me. This ensures that the answer I get is as objective as possible.
