the best image differencing ever

I had the pleasure today of reading two draft papers, one by Dun Wang on our alternative to difference imaging based on our data-driven pixel-level model of the Kepler K2 data, and the other by Huanian Zhang (Arizona) on H-alpha emission from the outskirts of distant galaxies. Wang's paper shows (what I believe to be) the most precise image differences ever created. Of course we had amazing data to start with! But his method for image differencing is unusual; it doesn't require any model of either PSF nor the difference between them. It just empirically figures out what linear combinations of pixels in the target image predict each pixel in the target image, using the other images to determine these predictor combinations. It works very well and has been used to find microlensing events in the K2C9 data, but it has the disadvantage that it needs to run on a variability campaign; it can't be run on just two images.

The Zhang paper uses enormous numbers of galaxy-spectrum pairs in the SDSS spectroscopic samples to find H-alpha emission from the outskirts of (or—more precisely—angularly correlated with) nearby galaxies. He detects a signal! And it is 30 times fainter than any previous upper limit. So it is big news, I think, and has implications for the radiation environments of galaxies in the nearby Universe.


  1. The Wang method sounds a bit like the Bramich (2008) method - though described with somewhat different language. Does Wang's method go far beyond that? I always preferred that general approach to e.g. Allard's ISIS even if it was a little more costly.

    1. Wang's method does go beyond Bramich 2008, because the result of the regression is not interpretable as a kernel at all. Also, in Bramich, the attempt is still to use image 1 to predict image 2. In Wang, the attempt is to use other pixels from image 2 to predict image 2, using images 1, 3, 4, 5, and so on as guides. In this sense, it is completely different, conceptually.

    2. ps. Thank you -- I modified slightly the text of the post to make it more accurate. I realize my language was totally wrong!