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

MSPA-AST：TB级数据流时代的图像相加、相减和源检测

• 批准号: 0709394
• 负责人: Andrew Connolly
• 金额: $ 35万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0709394&HistoricalAwards=false
• 关键词: MSPA; AST; Image; Coaddition; Subtraction

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.

AST-0709394康诺利目前的宇宙和谐模型包含，作为对能量预算的最重要贡献，某种形式的“暗能量”，需要解释膨胀的明显加速。 尽管暗能量的重要性显而易见，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科学中所有悬而未决的问题中最引人注目的。 一些雄心勃勃的宽视场光学和红外成像调查将解决有关暗能量和暗物质的问题，但它们的成功关键取决于它们能多好地探测源，表征星系的光度特性和形状，识别图像中的常见和不寻常特征，以及控制误报检测的数量。 对于在不同条件下几乎是真实的时间内观测到的涵盖多个波长的图像，必须做到这一点，而下一代调查的数据率将增加数千倍。 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和减，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结果对异常物体进行可靠的探测，这项工作不仅将提高目前和计划中的天文学宽视场成像调查的源探测质量，而且也将有益于其他领域。 物理和生物科学正面临着数据的指数增长率，并将需要快速和有效的图像分析技术来表征源的分布，并识别图像之间的变化。