The current concordance model for the Universe contains, as the most significant contribution to the energy budget, some form of 'dark energy', needed to explain the apparent acceleration of the expansion. Despite this evident importance, there are no compelling theories that explain either the energy density or the properties of the dark energy. The nature of dark energy ranks among the very most compelling of all outstanding problems in physical science. A number of ambitious wide-field optical and infrared imaging surveys will address questions about dark energy and dark matter, but their success depends critically on how well they can detect sources, characterize the photometric properties and shapes of galaxies, identify common and unusual features within images, and control the number of false positive detections. This must be achieved for images covering multiple wavelengths, observed under different conditions, and in almost real time, compounded by the thousand-fold increase in the data rate of this next generation of surveys. This project will develop state-of-the-art statistical and image analysis methods for the next generation of large area astronomical surveys. It will include the co-addition and subtraction of images taken over the period of a year, the identification and classification of sources within these images, and the robust detection of anomalous objects relative to an earlier set of observations.
The work will not only improve the quality of source detection for current and planned astronomical wide-field imaging surveys, but also benefit other fields. The physical and biological sciences are facing an exponential rate of growth of data, and will need fast and efficient image analysis techniques for characterizing the distribution of sources, and for the identification of variations between images.
