Intercluster filaments of galaxies programme: Pilot study survey and results

We present results from a pilot study of a new wide-field, multicolour (BVR) CCD imaging project, designed to examine galaxy evolution along large-scale filaments that connect clusters of galaxies at intermediate redshifts (0.07 < z < 0.20). Our pilot data set is based on 0.56 deg(2) of observations targeted on Abell 1079 and Abell 1084 using the Wide Field Imager on the Anglo-Australian Telescope. We describe our data reduction pipeline and show that our photometric error is 0.04 mag. By selecting galaxies that lie on the colour-magnitude relation of the two clusters we verify the existence of a low-density (similar to3-4 Mpc(-2)) filament population, conjoining them at a distance of > 3r(Abell) from either cluster. By applying a simple field correction, we characterize this filament population by examining their colour distribution on a (V-R)-(B-V) plane. We confirm the galaxian filament detection at a 7.5 sigma level using a cut at M-V = -18 and we discuss their broad properties.

Mathematical Morphology: Star/Galaxy Differentiation & Galaxy Morphology Classication

We present an application of Mathematical Morphology (MM) for the classification of astronomical objects, both for star/galaxy differentiation and galaxy morphology classification. We demonstrate that, for CCD images, 99.3 +/- 3.8% of galaxies can be separated from stars using MM, with 19.4 +/- 7.9% of the stars being misclassified. We demonstrate that, for photographic plate images, the number of galaxies correctly separated from the stars can be increased using our MM diffraction spike tool, which allows 51.0 +/- 6.0% of the high-brightness galaxies that are inseparable in current techniques to be correctly classified, with only 1.4 +/- 0.5% of the high-brightness stars contaminating the population. We demonstrate that elliptical (E) and late-type spiral (Sc-Sd) galaxies can be classified using MM with an accuracy of 91.4 +/- 7.8%. It is a method involving fewer 'free parameters' than current techniques, especially automated machine learning algorithms. The limitation of MM galaxy morphology classification based on seeing and distance is also presented. We examine various star/galaxy differentiation and galaxy morphology classification techniques commonly used today, and show that our MM techniques compare very favourably.