Using atlas and blas on mac OS X

This post might seem kind of specific, but I've decided its better to blog about what I'm actually doing than not blog at all. I spend most of my time on technical things in order to get to the higher level physics ideas. And this information is pretty useful.

A simple way to get atlas/blas working on mac OS X using the libraries that come with XCode is to link the headers to the "usual" place under /usr. This way you don't have to keep remembering where the header file is, and its buried deep.

sudo ln -s /System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Versions/Current/Headers/cblas.h /usr/include/cblas.h

And remember when you link to get the order right: -lcblas -latlas

Erin

DES weak lensing pipeline

I'm working on the lensing pipeline for estimating the shear from galaxy shapes in DES. We've already got a piece in place for measuring shapelet expansions of PSF stars. Now we need to finish a few other things. It breaks down like this currently:

• Mike has a code working for measuring the pre-PSF shear from a stack of images. The inputs are the positions of objects in the images and shapelets representations of the PSF in each image.
  
  
• I've got the following data-management related pieces in place
  
  
  
  • Can automatically locate all coadd and single epoch (SE) images on the local machine given only a tilename. This involves interaction with the database and all the pieces of metadata must be in place, so this was actually a non-trivial test of the system.
    
  • Can do the forward and reverse coordinate transformations image <-> sky based on the WCS info in the header including the distortion model. This is key considering our plan of action: for the shear measurement we will take the list of coadd objects and go back to the original SE images that were used in the coadd creation. So we need to take the position and bounding box from the coadd, convert to sky coordinates, and then convert back to image coordinates in the individual images. Due to the polynomial distortion model of scamp I implemented a simple root solver for this. Slow but accurate.
    
  
  

We still need to implement the following:

• PSF interpolation scheme. An optimal way to interpolate the PSF info. This will be a Jarvis&Jain style scheme that will improve over time.
  
• Figure out how to extract the proper PSF information and package it up in a way that is usable.
  
• Connect my data management tools, WCS transformation tools, and Mike's shear code into something that can automatically process a DES coadd tile, extract shear info, and get that into the database. At first this will do something kind of fake for the PSF, like taking that of the closest star but eventually will use the full PSF interpolation scheme.
  

Erin

Brookhaven job

I have accepted a position at Brookhaven National Lab. I posted about it in my other blog

M/L modeling; papers accepted

M/L Modeling

I'm creating some new measurements that make it simpler for Jeremy Tinker to model our mass-to-light ratio (M/L) results. To create his models he uses Zehavi's correlation functions for r-band selected samples. In my M/L paper I used i-band selected galaxies. It turns out the only way this modeling was going to get done is if I repeat my measurements for r-band limited galaxies. It took a week of running on a cluster of computers here at NYU and I now have the basic light measurements done. This was much faster than the previous measurements because I only measured the light correlation functions to 2Mpc. Now I need to do the analysis, which I hope to get to after my trip to Brookhaven this week.

Papers Accepted

Paper I, the cluster lensing measurement paper, and Paper III, the M/L paper were both accepted to ApJ this week. I've spent the last few days working with ApJ to create downloadable data tables that will accompany the papers, and accompanying "preview" tables to go in the papers. I also plan to make the data available online with links placed with the arXiv abstract.

Cluster Centers and Mass Scatter

Centers

One of the uncertainties in understanding the lensing results for the MaxBCG cluster sample is the center of mass. We know that some of the time our chosen center, the location of the brightest cluster galaxy, is not at the mass peak or the center of mass. On the other hand, the theoretical predictions for dark matter mass profiles are predicted around the mass peak.

We have put together two alternative centers, both based on finding a peak in smoothed density maps. One is in the number density and the other is in the luminosity weighted number density. It looks like we can flag bad centers when neither of these new centers agrees with the BCG location. Evidence comes from the X--ray concentration and also from lensing. I compared the mass profiles for "good" and "bad" centers at fixed cluster luminosity and found significant differences in the profiles. We still haven't figured out what is the best center, but we can now flag bad ones with some level of confidence.

Mass Scatter

The variance in the mass measurements we get from lensing are the convolution of many different sources of scatter. There is the scatter in mass at fixed richness, which is what we are interested in. But on top of this is the scatter from measurement noise sigma_err and the intrinsic variance among galaxy shapes for the background sources sigma_SN. These two sources of scatter are well known. But even on top of that is the variance from unknown sources of systematic error. We can guess at what these are, such as variance in the signal calibration as a function of observing conditions.

I looked at the inferred scatter sigma_tot^2 - sigma_err^2 - sigma_SN^2. I found that the residual scatter is much larger than the expected sigma_m^2 based on other measurements, such as X--ray properties and velocity measurements.

Next I want to figure out how to estimate other sources of variance. The goal is to design a measurement that does not also include the variance in mass of the lenses.