So after reading Beth's comment on yesterday's post I realized that the cut I was using DAOphot to do was round and sharp when I wanted chi and sharp. And I also didn't need DAOphot to do this because the allstar outputs contain chi and sharp.

I went back to see what limits I should put on chi. I used 'find' to calculate flux and plotted chi and sharp against flux like before. I then selected some limits. I implemented them to the un-calibrated matching code, matching only ra's and dec's that already made the cut.

The numbers:

I started with a g catalog of 27334 stars and an r catalog of 33679 (from the allstar outputs). After the magnitude cuts I had 22642 and 26525 stars, respectively.

All stars:
Matching the g and r band catalogs I was left with 16857 stars in the CMD, all of which agreed with stars matched by Python. After matching this catalog to SDSS I was left with 1359 stars for the final calibrated CMD.

Within 5 arcsec of Wil1's center:
After matching the g and r stars I was left with a catalog of 1356 stars, all of which agreed with stars that python matched. Then after matching this KPNO catalog with one of 25000 SDSS stars I was left with 165 stars for the calibrated CMD.


In either CMD I'm still not getting even as deep as the 2006 CMD. My main line of thinking at the moment has to do with the differences in read noise and gain that were input to DAOphot now and back then. My read noise is higher and gain is smaller than those used for the 2006 paper. While I stacked the exposures so I should have a deeper image, if I'm not mistaken a higher read noise will result in fewer sources detected and a low gain will just compound this effect. I'm not sure how I could quantify the affect this might have on the number of sources I'm finding, but I'm considering running DAOphot with the 2006 numbers to compare the resulting CMDs. However, I'm still confused about where the 2006 values came from and so I'm more or less convinced that mine are at least more correct. Though if I end up getting a CMD with better results I could be persuaded to change the values if Beth can explain where they came from...



Tomorrow:
In lieu of other ideas to make the CMD deeper, I think I'll forge ahead on the CMD calibration code. I still need to:

1. Weight the points in the plot by their measurement uncertainties.
2. Plot gtrue-ginst vs. gtrue=rtrue and do a linear fit on the result, solving for the zero-point and the color term in: gtrue-ginst=zp+ct(g-r).
3. Calculate the measurement error on each component of the zp. (Get the SDSS error from that catalog.)
4. Bootstrap the data 1000 times, saving each result in a structure. Then compare the variance in colors.

I doubt that all of this will get done tomorrow, but perhaps by the end of Monday.