Probing the Outer Solar System: Searching Below the Noise

探测外太阳系：在噪音之下进行搜索

• 批准号: 2107800
• 负责人: Andrew Connolly
• 金额: $ 42.13万
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107800&HistoricalAwards=false
• 关键词: Probing; Outer; Solar; System; Searching

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.

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，用于探测跨海王星天体(TNO)和主带小行星，该工具可以与鲁宾天文台的遗留空间和时间调查(LSST)数据一起使用。新一代天文测量，如LSST，将以前所未有的深度和细节绘制天空图，从而很快开启太阳系科学的新纪元。仅LSST一项就将使已知的TNO数量从目前的2729个增加到40,000多个，主带小行星的测量轨道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(要求在单独的图像中检测到小行星和彗星)。通过合并或合并这些调查的图像以提高信噪比，LSST探测到的小型物体的数量可能会增加一个数量级以上。该项目将开发建立在现有原型上的代码来处理这些图像。该工具将在完成后提供给社区使用。这个项目的更广泛的影响是针对本科生开发天文数据科学的教程和教材。作为课程的一部分，将使用华盛顿大学的Pre-MAP计划(该计划针对STEM中传统上代表性不足的群体，在他们第一年就介绍他们进行研究)来开发、制作和评估一系列教育模块(教程、木星笔记本、视频和天文应用程序)。该项目旨在开发开源软件，以帮助从即将到来的全天候调查(如LSST)中检测大型数据集中的移动物体。KBMOD(基于内核的移动对象检测)的原型可以在不到一分钟的时间内使用消费级GPU在10-15 4Kx4K图像堆栈中搜索超过100亿个运动对象轨迹。该项目将以KBMOD的成功为基础，开发一个能够在LSST的多年期调查中寻找TNOs的框架；根据包括神经网络在内的先进统计技术，开发一个工具集，根据小行星和彗星的光曲线对它们进行强有力的分类；实施优化的搜索算法和战略，以便在具有长时间基线的调查中搜索移动源和更快的移动源(例如主带小行星)；并将概率框架扩展到图像分类问题--最初专注于识别勉强解析的小行星图像中的二元性。这一奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Sifting through the Static: Moving Object Detection in Difference Images

筛选静态：差异图像中的运动物体检测

• DOI: 10.3847/1538-3881/ac22ff
• 发表时间: 2021
• 期刊: The Astronomical Journal
• 作者: Smotherman, Hayden;Connolly, Andrew J.;Kalmbach, J. Bryce;Portillo, Stephen K.;Bektesevic, Dino;Eggl, Siegfried;Juric, Mario;Moeyens, Joachim;Whidden, Peter J.
• 通讯作者: Whidden, Peter J.