Crowdsourcing the Sky with the NOIRLab Source Catalog

使用 NOIRLab 源目录众包天空

• 批准号: 2109196
• 负责人: David Nidever
• 金额: $ 35.14万
• 依托单位: Montana State University
• 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=2109196&HistoricalAwards=false
• 关键词: Crowdsourcing; Sky; NOIRLab; Source; Catalog

The all-sky NOIRLab Source Catalog (NSC) is a catalog of nearly all the data, taken by astronomers over many years that is resident in NOIRLab’s image archive. This data includes information on three quarters of the sky and is available for exploration with innovative big data techniques that will also be useful for other large surveys such as that to be obtained with the Rubin Observatory. This project will use the database to address two important science questions regarding asteroids and pulsating stars. There are many asteroids in our solar system, some of which are nearby and could potentially impact the earth. Astronomers have been trying to catalog most of them over the last couple of decades. The NSC can help with this effort as it is able to detect fainter asteroids than most current searches and covers parts of the sky not previously explored. Large scale structures in our Milky Way galaxy can be mapped and explored by finding certain types of pulsating stars for which distances can be accurately determined from previous studies. The project includes a plan to involve tens of thousands of citizen scientists to assist in various aspects of the project notably to find stars that have change position on the sky.New techniques are needed to extract important scientific information and insights from both existing data sets and future large data such as the Legacy Survey of Space and Time (LSST) at the Rubin Observatory. The all-sky NOIRLab Source Catalog (NSC) is a catalog of nearly all public data in NOIRLab’s image archive. The NSC covers three quarters of the sky going substantially deeper than previous projects and includes data in areas never before cataloged. The project looks to explore the NSC to develop big data techniques focused on two science cases. The first of these is to search for high proper motion objects, especially Near-Earth Objects that could potentially impact the earth. Modern clustering algorithms such as Spectral Clustering and DBSCAN will be utilized to process the 68 billion measurements and provide a census of nearby objects. The second science investigation will be on searching for stellar variability. The time-series nature of the NSC will permit a search for objects that vary in brightness over time. This includes variable stars that are good tracers of the structure of the Milky Way. Non-parametric period estimation techniques and new variable star templates will be utilized to enable automatic and robust identification and classification of all variables in the NSC. An extensive citizen scientist program is planned for assistance in the detection of proper motions of stars.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.

全天空NOIRLab源目录(NSC)是天文学家多年来获取的几乎所有数据的目录，保存在NOIRLab的图像档案中。这些数据包括四分之三天空的信息，可用于利用创新的大数据技术进行探索，这些技术也将对其他大型调查有用，如将通过鲁宾天文台获得的调查。该项目将使用该数据库来解决关于小行星和脉动恒星的两个重要科学问题。在我们的太阳系中有许多小行星，其中一些就在附近，可能会影响地球。在过去的几十年里，天文学家们一直在试图对其中的大多数进行分类。NSC可以帮助这一努力，因为它能够探测到比大多数当前搜索工作更暗的小行星，并覆盖以前没有探索过的部分天空。我们银河系的大尺度结构可以通过找到某些类型的脉动恒星来绘制和探索，这些恒星的距离可以从以前的研究中准确地确定出来。该项目包括一项计划，让数万名公民科学家协助该项目的各个方面，特别是寻找在天空中位置发生变化的恒星。需要新的技术来从现有数据集和未来的大型数据中提取重要的科学信息和见解，例如鲁宾天文台的遗留时空调查(LSST)。全天空NOIRLab源目录(NSC)是NOIRLab图像档案中几乎所有公共数据的目录。NSC覆盖了四分之三的天空，比以前的项目要深得多，并包括了以前从未编目过的地区的数据。该项目旨在探索国家科学委员会，以开发专注于两个科学案例的大数据技术。第一个是搜索高自行物体，特别是可能影响地球的近地物体。光谱聚类和数据库扫描等现代聚类算法将被用来处理680亿个测量值，并提供对附近物体的普查。第二项科学调查将是寻找恒星的可变性。NSC的时间序列特性将允许搜索亮度随时间变化的对象。这包括可以很好地追踪银河系结构的变星。将使用非参数周期估计技术和新的可变星形模板，以实现对国家统计委员会中所有变量的自动和稳健的识别和分类。一个广泛的公民科学家计划帮助探测恒星的自身运动。这一奖项反映了NSF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

Exploring the Solar System with the NOIRLab Source Catalog I: Detecting Objects with CANFind

• DOI: 10.3847/1538-3881/ac2230
• 发表时间: 2021-08
• 期刊: The Astronomical Journal
• 作者: K. Fasbender;D. Nidever

Second Data Release of the All-sky NOIRLab Source Catalog

• DOI: 10.3847/1538-3881/abd6e1
• 发表时间: 2020-11
• 期刊: The Astronomical Journal
• 作者: D. Nidever;A. Dey;K. Fasbender;S. Juneau;A. Meisner;Joseph Wishart;A. Scott;Kyle Matt;R. Ni