Adaptive spatial binning of integral-field data using galaxy morphology
Sam Vaughan
University of Oxford
We present a novel method to adaptively bin integral-field spectrograph (IFS) observations of galaxies in order to reach a minimum signal-to-noise (SN) ratio per bin. By keeping the morphology of the galaxy at the forefront during the binning process, the distribution of bins better follows the various morphological components and minimises the mixing of components in each bin.

The galaxy light is first divided into elliptical shells, which follow the variation of ellipticity within the galaxy. The flux in each elliptical shell is then adaptively split into angular sectors. An equilateral constraint helps maintain a roughly equivalent radial and angular extent for each bin. Whilst the target SN is cannot be strictly enforced in this approach, the small scatter in output SN is a small cost for significantly reduced spectral mixing of morphological components.

This technique has wide applications to current and future IFS surveys, and we show examples of data from various surveys and instruments spanning different morphological types. We also discuss the speed and memory requirements, and the scope of applying this binning method to large data cubes.