Probing the Outer Solar System: Searching Below the Noise

来自华盛顿大学的这个研究小组将开发一种图像处理工具，用于检测跨海王星天体（TNO）和主带小行星，这些小行星可以与鲁宾天文台的时空遗产调查（LSST）数据一起使用。LSST等新一代天文观测将很快开启太阳系科学的新时代，以前所未有的深度和细节绘制天空。仅LSST就将使已知的TNO数量从今天已知的2729个增加到40，000多个，并且具有测量轨道的主带小行星的数量也将增加一个数量级。目前的分析技术只利用了这些数据中存在的一小部分信息（要求在单个图像中检测小行星和彗星）。通过组合或共同添加这些调查的图像以提高信噪比，LSST检测到的小天体数量可以增加一个数量级以上。该项目将在现有原型的基础上开发代码来处理这些图像。该工具将在完成后提供给社区。该项目的更广泛影响是为本科生编制天文数据科学教程和教材。作为该课程的一部分，一系列教育模块（教程，笔记本电脑，视频和天文应用程序）将被开发，原型，并使用华盛顿大学的Pre-MAP程序进行评估（一个针对传统上在STEM领域代表性不足的群体的项目，在第一年就将他们引入研究）。该项目旨在开发开放的源软件，以帮助检测移动物体的大型数据集，从即将到来的全天空调查，如LSST。KBMOD（基于内核的移动对象检测）的原型可以在一分钟内使用消费级GPU在10-15个4Kx 4K图像中搜索超过100亿个移动对象轨迹。该项目将在KBMOD成功的基础上，开发一个能够在LSST的多年调查中发现TNO的框架;开发一套工具，用于根据包括神经网络在内的先进统计技术，根据小行星和彗星的光变曲线对其进行可靠的分类;实施优化的搜索算法和策略，以便在具有长时间基线的调查中搜索移动源和快速移动源该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

This research team from the University of Washington will develop an image processing tool for to detect Trans Neptunian Objects (TNOs) and Main Belt Asteroids that can be used with Rubin Observatory’s Legacy Survey of Space and Time (LSST) data. New generations of astronomical surveys such as LSST will soon open a new era in Solar System science by mapping the skies in unprecedented depth and detail. LSST alone will increase the number of known TNOs from the 2729 known today to over 40,000 and the number of Main Belt Asteroids with measured orbits will also increase by an order of magnitude. Current analysis techniques make use of only a fraction of the information present within these data (requiring that asteroids and comets be detected in individual images). By combining or coadding images from these surveys to increase the signal-to-noise ratio, the number of small bodies detected by the LSST could increase by over an order of magnitude. This project will develop codes building on an existing prototype to process these images. The tool will be made available to the community upon completion. The broader impacts of this project are to develop tutorials and educational material for astronomical data science targeted towards undergraduate students. As part of this curriculum, a series of educational modules (tutorials, Jupyter notebooks, videos, and astronomical applications) will be developed, prototyped, and evaluated using the Pre-MAP program at the University of Washington (a program that targets traditionally underrepresented groups in STEM by introducing them to research in their first year).This project is to develop open-source software to aid in the detection of moving objects in large data sets from upcoming all-sky surveys, such as LSST. A prototype of KBMOD (Kernel-Based Moving Object Detection) can search over 10 billion moving object trajectories in a stack of 10-15 4Kx4K images in under a minute using consumer-grade GPUs. This project will build upon the success of KBMOD to develop a framework capable of finding TNOs in the multi-year surveys from the LSST; develop a tool set for the robust classification of asteroids and comets from their light curves based on advanced statistical techniques including neural networks; implement optimized search algorithms and strategies for searching for moving sources in surveys with long temporal baselines and for faster moving sources (e.g. Main Belt Asteroids); and extend the probabilistic framework to the question of image classification - initially focusing on the identification of binarity in barely resolved images of asteroids.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.