Adaptive binning of X-ray galaxy cluster images

We present a simple method for adaptively binning the pixels in an image. T he algorithm groups pixels into bins of size such that the fractional error on the photon count in a bin is less than or equal to a threshold value, a nd the size of the bin is as small as possible. The process is particularly useful for generating surface brightness and colour maps, with clearly def ined error maps, from images with a large dynamic range of counts, for exam ple X-ray images of galaxy clusters. We demonstrate the method in applicati on to data from Chandra ACIS-S and ACIS-I observations of the Perseus clust er of galaxies. We use the algorithm to create intensity maps, and colour i mages that show the relative X-ray intensities in different bands. The colo ur maps can later be converted, through spectral models, into maps of physi cal parameters, such as temperature, column density, etc. The adaptive binn ing algorithm is applicable to a wide range of data, from observations or n umerical simulations, and is not limited to two-dimensional data.