Spectrophotometry: Imaging with custom narrow-band filters and an automated data-reduction pipeline

Abundance variations of carbon and nitrogen in globular star clusters provide astronomers with a means to determine a cluster's evolutionary past. Moreover, these clusters are so ancient (∼13 billion years) and so well preserved that they provide an ideal diagnostic for the overall chemical history of the Milky Way Galaxy. Traditionally, spectroscopy is the preferred method to perform investigations into such theories. However, it is not without its drawbacks: spectroscopy can normally only be obtained star by star, and both large telescopes and a great deal of time is required to carry out research in this manner. As globular clusters are known to contain up to a million stars, studying each star individually would take too much time to return a true representative sample of the cluster stars. So, we opt instead for a spectrophotometric technique and a statistical approach to infer a cluster's composition variations. This has required the design and use of new custom narrow- band filters centered on the CH and CN molecular absorption bands or their adjacent continua. Two Galactic clusters (M71 & M92) with contrasting characteristics have been chosen for this study. In order to process this data a header-driven (i.e. automated) astronomical data-processing pipeline was developed for use with a family of CCD instruments known as the FOSCs. The advent of CCD detectors has allowed astronomers to generate large quantities of raw data on a nightly basis, but processing of this amount of data is extremely time and resource intensive. In our case the majority of our cluster data has been obtained using the BFOSC instrument on the 1.52m Cassini Telescope at Loiano, Italy. However, as there are a number of these FOSC instruments throughout the world, our pipeline can be easily adapted to suit any of them. The pipeline has been tested using various types of data ranging from brown dwarf stars to globular cluster images, with each new dataset providing us with new problems/bugs to solve and overcome. The pipeline performs various tasks such as data reduction including image de-fringing, image registration and photometry, with final products consisting of RGB colour images and colour magnitude diagrams (CMD).