The production of photometric light curves from astronomical images is a verytime-consuming task. Larger data sets improve the resolution of the lightcurve, however, the time requirement scales with data volume. The data analysisis often made more difficult by factors such as a lack of suitable calibrationsources and the need to correct for variations in observing conditions from oneimage to another. Often these variations are unpredictable and corrections arebased on experience and intuition. The High Efficiency Image Detection & Identification (HEIDI) pipelinesoftware rapidly processes sets of astronomical images. HEIDI automaticallyselects multiple sources for calibrating the images using an algorithm thatprovides a reliable means of correcting for variations between images in a timeseries. The algorithm takes into account that some sources may intrinsicallyvary on short time scales and excludes these from being used as calibrationsources. HEIDI processes a set of images from an entire night of observation,analyses the variations in brightness of the target objects and produces alight curve all in a matter of minutes. HEIDI has been tested on three different time series of asteroid 939 Isbergaand has produced consistent high quality photometric light curves in a fractionof the usual processing time. The software can also be used for other transientsources, e.g. gamma-ray burst optical afterglows. HEIDI is implemented in Python and processes time series astronomical imageswith minimal user interaction. HEIDI processes up to 1000 images per run in thestandard configuration. This limit can be easily increased. HEIDI is nottelescope-dependent and will process images even in the case that no telescopespecifications are provided. HEIDI has been tested on various Linux . HEIDI isvery portable and extremely versatile with minimal hardware requirements.
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