I seem to have slowed down after my initial daily and even twice daily blog posts. So now I'll try to summarize what I've been doing for the past week.
I'm pretty sure I spent most of the time simulating perfect Gaussians and then running Nmix on the data to find the single component confidence level. I then repeated this 100 times and found the average value. I then repeated the whole process for a different number of stars to see just how many stars are necessary for Nmix to be reasonably accurate. The results weren't too satisfying, as it seems that we need around 175 for Nmix to have 70% or so single component confidence.
This is unfortunate, as most of the samples that I have seen only have 20-60 stars, way under the limit wherein Nmix can actually determine that there is only a single component.
I then repeated this, adding in some randomly generated error to the simulated Gaussian, to see if this changed its ability to detect shape. This had practically no effect on the Nmix results, which makes some sort of sense.
On Wednesday, Marla Geha was visiting Beth and she sent me the velocity distributions for 8 other dwarf galaxies (after several reminders). I'll do something with them once I finish with the simulated data.
My newest project was to simulate a sample composed of two Gaussians to see if Nmix actually works the way it should in detecting multiple structures. I placed the two Gaussians one standard deviation apart. This time it was even less accurate than with the single Gaussian at finding the correct structure. I managed to stupidly delete a bunch of that data, but there was only about 40% confidence for 2 components with under 200 stars. With 1000 stars there was on average about 70% confidence, which indicates that it did work. But we don't have nearly that many stars in our samples so it's pretty discouraging.
I also started testing the kurtosis of various sampled data and the actual samples and will be attempting to figure out how to effectively use that in the near future (or the next time the power doesn't randomly shut off in the middle of my program).
1) Add kurtosis to double Gaussian test
2) Read Simon and Geha 2007
3) Do the ratio stuff for double Gaussian