Big Mock Run

I am performing several test to figure out what is wrong with my 3D correlation functions. The first is to run on a bigger mock data set.

I had a discussion with Nic Ross today about the proper way to deal with populating randoms. I was starting to fear that I was doing something wrong in that I force the randoms to have the same distribution in the "redshift" dimension. I was told to do this by David and Nikhil, but wondered if this could be the cause of the problem. Nic assured me that this was the right thing to do, and suggested I read the following papers about correlation functions:

The 2dF Galaxy Redshift Survey: correlation functions, peculiar velocities and the matter density of the Universe

The 2dF QSO Redshift Survey - XIV. Structure and evolution from the two-point correlation function

The 2dF-SDSS LRG and QSO Survey: the LRG 2-point correlation function and redshift-space distortions

Nic looked at my 3D correlation functions and agrees that there is something fishy about them.

In terms of the bigger run:
I am doing the same comparison of Alexia's and my correlation 3D correlation functions that I did yesterday, but on a much bigger box/sphere. My data is contained in a sphere with radius 471.0 Mpc/h (half of the box). Alexia's data is the entire box.

Some plots of the data masks:

I am worried that some of the geometry effects will come into play when I compare the correlation functions because

1. Alexia's box has more points (corners of the box)
2. Alexia's box is a box, while mine is a sphere
3. When you look at a histogram of the distribution of the declinations, it is not flat (you would expect this due to there being more objects in the center of the sphere when dec is ~pi/2 (versus the top or bottom or top of the sphere). However, when I populate the randoms I think I populate them uniformly... need to check this.

This takes a while to run because there are 300k objects in each box. Results to follow...