Another belated update. (I haven't been much in the mood for blogging lately for some reason.)

So--ARTIFICIAL STAR TESTS ARE DONE. Yay. They're finally looking up to snuff so I'm pretty pleased. Definitely excited to be sleeping more this week than last. All of the artificial star data has been compiled into masterlists and calibrated. I've also calculated the completeness limit from all of these runs put together. Check it out! I was a little disappointed at first by the completeness levels of the bright end. I'd like them to all be 100% across the board. Beth pointed out that at the bright end there's some shot noise due to low number statistics (there are only a handful of stars in some of the brightest bins. Additionally, my chi/sharp cut begins to get a lot looser around r=22.5 which explains the bump in completeness there. In general, I'm happy with the completeness.

Next on the agenda were the ML results. I started out with the idea that I would compare the results from data down to the 75% (r ~ 25.) completeness level to those results as deep as the 90% (r ~ 24.75) completeness level. Unfortunately, both of these looked terrible. (Also unfortunately, I can't seem to upload more than one image right now.)

So the first thing I did was repeated the ML calculation using a shallower set--I went back to the trusty r < 24.25 which I had used for my thesis. This data was still not great but it was better, as expected. At this point I had to go back to the drawing board and was getting worried that something was seriously wrong. Then I remembered the conversations with Ricardo about the initial conditions for the ML calculation. Small numbers play a role in screwing with ML results, but Ricardo had previously mentioned that initial conditions likely play an even bigger part in changing results. I have more stars in my masterlist than ever before, so I put my chips on the initial conditions. I went back and repeated the ML calculation iteratively for several magnitude limits, each time using the result of the last as the input for the next one. At r < 24.25, the results converged after only 3 iterations and at r < 24.75, the results converged after 4 iterations and were looking great. The two were also consistent with one another so things are looking good.


I spoke with Beth about which magnitude limit to choose and she suggested I next go to the morphology to see how those were looking at each of the magnitude limits. First, I checked that they were sane. One concern was the the calculated position angle is now much lower than previous calculations (~62 degrees as opposed to Martin et al. 2008's 77 degrees). An overplotted ellipse showed that PA looks good and the shape of Wil1 was recognizable even with many more stars than we'd previously had. The choice of magnitude limit has thus come down to signal to noise. A visual inspection of the morphology at r<24.25 and r<24.75 suggests a higher S/N in the shallower data. In fact, my signal-to-noise calculation revealed that S/N is better by a factor of 10 with a maglim of r=24.25. The reason for this is that the number of galaxies is increasing so rapidly at fainter magnitudes that signal is getting washed out more at the fainter levels.

I've now essentially decided on a magnitude limit of r =24.25 for my further calculations. My next steps are:

1. Calculate the photometric uncertainty as a function of magnitude from the artitificial star data.

2. Incorporate this photometric uncertainty and the isochrone/fiducial uncertainty into the distance code. Beth suggested that I use either the minimum photometric error or 0.05, whichever is larger, as the expected uncertainty in our model isochrones and empirical fiducials.

3. I'll then calculate the distance to Wil1 and the best fit main sequence model.

4. Using the results of the distance calculation, I'll more carefully choose the stars that are being used to describe the shape of Wil1. I'll also experiment with different smoothing lengths in an attempt to maximize noise. At the moment I'm getting a maximum signal of 15 sigma over the background level.

5. After the morphology is finished, I'll move on to things like the final absolute magnitude and surface brightness calculations.  We'll also want to simulate fake Wil1's to compare the results and calculate the assymetry parameter. Then I should be home free for putting results and conclusions in the paper and cleaning up my code next week.