Collaborative Research:SLINGSHOT:Decoding Dark Matter through Gravitational Lensing

合作研究：弹弓：通过引力透镜解码暗物质

• 批准号: 2108866
• 负责人: Stephon Alexander
• 金额: $ 32.07万
• 依托单位: Brown 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=2108866&HistoricalAwards=false
• 关键词: Collaborative; Research; SLINGSHOT; Decoding; Dark

The effects of dark matter can be seen in gravitationally lensed systems where light from a distant galaxy is gravitationally lensed by a foreground galaxy. Analysis of the resulting distorted, magnified, multiple images of the background galaxy provide clues to the nature of dark matter. The principal investigators (PIs) Gleyzer, Ames and Alexander will develop the Strong Lensing Inference and Generative System for Harnessing Observational Telescope Data (SLINGSHOT) framework for simulating realistic gravitational lensing images and applying machine learning algorithms to identify known categories of possible dark matter substructure. The PIs will broaden participation with a series of online lectures on machine learning and astrophysics as part of the National Society of Black Physicists Computational Physics Initiative. The team will also continue to teach data science concepts with Jupyter notebooks through their Creative Open Data Environment (CODER) education and outreach project for K-12 teachers and students.The PIs will establish a baseline for classification of dark matter substructure morphology using both conventional (Bayesian) and machine learning algorithms. They will train the machine learning algorithms in anomaly detection, classification and property estimation using a library of representative dark matter models. Unsupervised anomaly detection techniques will identify promising candidate images in a model-independent manner and localize anomalies for further analysis. The PIs will use the code to analyze gravitational lensing data from the Hubble Space Telescope and the Hyper Suprime-Cam on the Subaru Telescope, in preparation to apply their methods to survey data from the Vera C. Rubin Observatory. The SLINGSHOT toolkit will be made available to the astronomical community.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.

暗物质的效果可以在重力镜头系统中看到，在重力镜头系统中，来自前景星系在重力镜头上的光重力镜头。 分析所得的扭曲，放大，背景星系的多个图像为暗物质本质提供了线索。首席研究人员（PIS）Gleyzer，Ames和Alexander将开发强大的镜头推理和生成系统，用于利用观察望远镜数据（Slingshot）框架，用于模拟现实的重力镜头图像并应用机器学习算法，以确定可能的暗物质分组的已知类别。 PIS将通过一系列有关机器学习和天体物理学的在线讲座来扩大参与，这是国家黑人物理学家计算物理学计划的一部分。该团队还将通过其创造性的开放数据环境（编码器）教育和推广项目来教授K-12教师和学生的数据科学概念。PIS将建立使用常规（贝叶斯）和机器学习算法对暗物质亚结构形态进行分类的基线。他们将使用代表性暗物质模型库中的机器学习算法在异常检测，分类和属性估算中进行训练。无监督的异常检测技术将以模型独立的方式识别有希望的候选图像，并定位异常以进行进一步分析。 PI将使用该代码分析来自哈勃太空望远镜的重力镜头数据和Subaru望远镜上的超弹力板，以准备将其方法应用于Vera C. Rubin天文台的数据。弹弓工具包将提供给天文学界。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。