MSPA-AST:Image Coaddition, Subtraction and Source Detection in the Era of Terabyte Data Streams

目前的宇宙和谐模型包含了某种形式的“暗能量”，这是对能量收支最重要的贡献，需要它来解释宇宙膨胀的明显加速。尽管这一点很重要，但目前还没有令人信服的理论来解释暗能量的能量密度或性质。暗能量的性质是物理学中所有突出问题中最引人注目的问题之一。一些雄心勃勃的广域光学和红外成像调查将解决有关暗能量和暗物质的问题，但它们的成功与否关键取决于它们能在多大程度上探测到源，确定星系的光度学特性和形状，识别图像中常见和不寻常的特征，并控制误报检测的数量。必须对覆盖多个波长的图像实现这一点，这些图像是在不同的条件下观察到的，而且几乎是实时的，再加上下一代调查的数据速率增加了数千倍。该项目将为下一代大面积天文测量开发最先进的统计和图像分析方法。它将包括对一年期间拍摄的图像进行加减，对这些图像中的源进行识别和分类，以及相对于较早的一组观测对异常物体的稳健检测。这项工作不仅将提高目前和计划中的天文广域成像调查的源检测质量，而且还将有益于其他领域。物理和生物科学面临着数据的指数级增长，需要快速有效的图像分析技术来表征来源的分布，并识别图像之间的差异。

AST-0709394ConnollyThe 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.