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).
We describe how instrument data-processing pipelines can be quickly and easily developed using the modularity, header-manipulation, and scripting features of the IRAF suite. Our illustration case is the design of a simple IRAF-based reduction and analysis pipeline for the BFOSC instrument on the 1.52m Cassini Telescope at Loiano, run by the Osservatorio Astronomico di Bologna. On the basis of header keywords, raw frames are automatically processed in a series of steps: grouping by any observational parameter(s), CCD reduction, registration, coaddition, photometry, deconvolution, RGB-tricolour representation, and basic astrometry, with spectroscopy partially implemented as of now. In this way, FITS data can be automatically analysed from raw frames to "end product" (of final or near-final scientific quality), while still "at the telescope", thus enabling much faster feedback. Since the xFOSC family of instruments produced by the Astronomical Observatory of Copenhagen, which includes BFOSC, share identical design and operation, it should be simple to adapt the pipeline to any of the ten FOSC instruments: DFOSC on the ESO/Danish 1.54m, ALFOSC on the Nordic Optical Telescope, TFOSC on the new TT1 (Castelgrande) Telescope. We also aim to make it available for "on the fly" archival processing.