Kernel-Based Moving Object Detection

该奖项支持研究一种新的搜索算法，用于移动微弱物体，并将其应用于斯隆数字巡天II（SDSS-II）和暗能量巡天（DES）中的现有数据集，以发现太阳系中的新物体。 新的设计，称为基于内核的移动对象检测（KB-MOD），将计算速度提高100倍，并使用概率框架来帮助用户更好地决定何时发现新对象，以及何时达到限制，应该停止寻找。 灵敏度的提高将使发现更小、更远（因此也更暗）的外太阳系天体成为可能，这将有助于完成对海王星以外的外太阳系（包括柯伊伯带区域）的普查。 PI和他的团队将通过华盛顿大学的天文学专业预科项目（Pre-MAP）培训和指导传统上代表性不足的学生，该项目针对尚未宣布专业的学生，并将他们融入研究环境，以便他们更有可能主修科学。该提案的范围包括开发基于图像的概率分析基础设施，并建立和实施可扩展的方法，以使用来自广泛的潜在太阳系轨道的轨迹访问数据。 这项工作还将把SDSS和DES的科学数据整合到LSST数据管理环境中。

This award supports funding to work on a new search algorithm for moving faint objects and apply it to existing datasets in the Sloan Digital Sky Survey II (SDSS-II) and Dark Energy Survey (DES) in order to discover new objects in our solar system. The new design, called Kernel-Based Moving Object Detection (KB-MOD) will speed computations by a factor of a hundred, and use a probabilistic framework to help the user make better decisions about when a new object has been discovered, and when the limitations are reached and one should stop looking. The improved sensitivity will make it possible to discover smaller and more distant (and hence, fainter) outer solar system objects, which will help complete the census of the outer solar system, including the Kuiper Belt region, beyond Neptune. The PI and his team will train and mentor students from traditionally underrepresented groups through the University of Washington's Pre-Major in Astronomy Program (Pre-MAP), which targets students who have not yet declared a major and integrates them into the research environment so that they will be more likely to major in science. The scope of the proposal includes developing a probabilistic, image-based analysis infrastructure, and build and implement scalable methods to access the data using trajectories from a wide range of potential solar system orbits. The work will also integrate science data from the SDSS and DES into the LSST Data Management environment.