Title:Adaptive binning of X-ray galaxy cluster images

Abstract: We present a simple method for adaptively binning the pixels in an image. The 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, and the size of the bin is as small as possible. The process is particularly useful for generating surface brightness and colour maps, with clearly defined error maps, from images with a large dynamic range of counts, for example X-ray images of galaxy clusters. We demonstrate the method in application to data from Chandra ACIS-S and ACIS-I observations of the Perseus cluster of galaxies. We use the algorithm to create intensity maps, and colour images which show the relative X-ray intensities in different bands. The colour maps can later be converted, through spectral models, into maps of physical parameters, such as temperature, column density, etc. The adaptive binning algorithm is applicable to a wide range of data, from observations or numerical simulations, and is not limited to two-dimensional data.