I fixed the angular correlation function using the method outlined in my last post. I did a run again on the mock data using photometric and spectroscopic sets with 200,000 objects. The correlation functions look much better now (and the code runs much faster too!):

2D and 3D correlation functions

The 2D are actual angular separations (different from what Alexia was doing which was collapsing the third dimension onto a projection plane). This is super exciting because the noise has gone down a lot compared to what I was getting before. Hopefully this will mean a better reconstruction.

The distribution functions I applied to the two data sets are below:

The distribution functions I applied to the two data sets are below:

The cyan line is a histogram of the number photometric objects as a function of comoving distance away from the observer. The magenta line is the imposed photometric distribution function (what we are trying to reconstruct). The green line is a histogram of the number of spectroscopic objects as a function of comoving distance away from the observer. Note that it is different than the photometric distribution.

And here is the reconstruction:

And here is the reconstruction:

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