2006MNRAS.371..829S

We present a new technique for choosing spatial regions for X-ray spectroscopy, called `contour binning'. The method chooses regions by following contours on a smoothed image of the object. In addition, we re-explore a simple method for adaptively smoothing X-ray images according to the local count rate, we term `accumulative smoothing', which is a generalization of the method used by fadapt. The algorithms are tested by applying them to a simulated cluster data set. We illustrate the techniques by using them on a 50 ks Chandra observation of the Cassiopeia A supernova remnant. Generated maps of the object showing abundances in eight different elements, absorbing column density, temperature, ionization time-scale and velocity are presented. Tests show that contour binning reproduces surface brightness considerably better than other methods. It is particularly suited to objects with detailed spatial structure such as supernova remnants and the cores of galaxy clusters, producing aesthetically pleasing results.