More Reconstruction Test

I ran the reconstruction with a different selection function to see if that made things better. The photometric distribution is shown here (workingDir is here: 'run2011530_1632'):

I'm still getting the issue that I am having trouble finding phibins that don't give me the error. But here are some reconstructions, the first one looks good-ish, the second one not so much:

Note that the label on the y-axis is not right, there are more objects in each bin than it says, I just had to normalize them to be on the same scale.

I'm running again with small angles to see if this helps. This smaller angle run is here (workingDir = 'run2011531_1451').

The angular correlation functions are freaking out at small angles. Not sure what that is about:

They look normal starting around 10^-3:

I tried doing the reconstruction both using all the angular bins, and just the angular bins that weren't freaking out, the look the same:

less angular bins:

all angular bins:

Here are some other reconstruction binning, disturbingly I am still getting the "feature" where if I change the # of bins slightly (from 15 to 16) I get very different reconstructions:

Because I am still not sure how doing this "non uniform" binning in r effects the reconstruction, I am running again, but with uniform binning. We'll see how that goes.

Playing with Reconstruction

First selection function:
sf=selection(0.2,0.1,0.05,0.2,bins)


First phi:
Best Reconstruction:
Other reconstructions (not so good):

New selection function:
sf=gals.selection(0,0.05,0.3,10,bins)

New phi:
Best reconstruction:
Other reconstructions:

Back to Work (back to Reconstruction)

I need to do a better job of balancing working on both the Newman Project and BOSS Likelihood target selection. I feel like it's been months since I have worked on the Newman stuff. Luckily Alexia was in Mexico and helped me get up to speed again.

Martin White gave me a halo mock catalog (I was using the wrong one for a while) and I have applied the following selection function to the photometric data set:

This corresponds to the following redshift distribution (phi):

The blue line is a histogram of the redshifts of the mock data, and the green line is the phi we are imposing (by both geometry and the selection function, you can see they match pretty well). I believe the difference in these two is based on actual structure, not an problem with implementing the selection function.

On the spectroscopic data, I imposed no selection function, and so the distribution was based purely on the geometry and structure of the data. Because the geometry is a sphere, we expect more objects at greater redshifts (larger volume at greater r):

I decided to start with a small run with data sets of approximately equal size. The photometric data set has 25,887 objects, and the spectroscopic data set has 25,470 objects. The redshift bins are 50 Mpc/h wide and go from 0 - 500 Mpc/h. Here are the 2D cross-correlation functions and the 3D auto-correlation functions of the mock data:

Here is the reconstruction. It is using an old version of crl (I'm having trouble downloading the newest version from the repository). It doesn't seem to be doing that good of a job reconstructing:

Maybe updating crl will help things? Thoughts Alexia?

Mock Reconstruction

I ran a full reconstruction on the mock data over the past couple days. There are some problems with this run (which I discovered after I started it running, but decided to just let it finish). The first is that I have not fixed the distribution of the randoms in the 2D correlation function to go as cosine for the declinations (they are flat). The second is that I discovered the mock catalog I was using was not dark matter halos but actually LRGs and to the clustering properties are nonsensical. I have since gotten a dark matter halo catalog from Martin White.

Here are the 2D and 3D correlation function for the various redshift bins:

2D correlation functions for redshift bins


3D correlation functions for redshift bins

As you can see the 3D correlation function are no longer fluctuating around zero as they were on the Sloan data.

The reconstruction is a bit puzzling. Below is a histogram of the comoving distances (from the center of the sphere) of the spectroscopic (yellow), photometric (green) data sets as well as the reconstructed phi (redshift distribution (pink)). You will noticed that while the histograms don't match, the bumps in the photometric/spectroscopic histograms seem to match the bumps in phi. I was thinking that because these photometric/spectroscopic histograms include both the geometry and the "selection function" so to speak (I didn't actually apply a selection function, so it is just the natural clustering of the objects). Whereas phi (I think) is only supposed to be the selection function, not the geometry. I was wondering if perhaps if I subtract the geometry from the p/s histograms (which is proportional to r^2) then we would basically be left with these same wiggles? I've asked Alexia about this and I am waiting for a response.