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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

基本信息

批准号：
2108645
负责人：
Sergei Gleyzer
金额：
$ 36.6万
依托单位：
University of Alabama Tuscaloosa
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=2108645&HistoricalAwards=false
关键词：
Collaborative Research SLINGSHOT Decoding Dark

项目摘要

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.

暗物质的影响可以在引力透镜系统中看到，其中来自遥远星系的光被前景星系引力透镜。对背景星系扭曲、放大的多重图像的分析为暗物质的性质提供了线索。主要研究人员（PI）Gleyzer，艾姆斯和亚历山大将开发用于利用观测望远镜数据的强透镜推理和生成系统（SLINGSHOT）框架，用于模拟逼真的引力透镜图像，并应用机器学习算法来识别已知类别的可能暗物质子结构。PI将通过一系列关于机器学习和天体物理学的在线讲座扩大参与，作为全国黑人物理学家协会计算物理学倡议的一部分。该团队还将继续通过他们的创意开放数据环境（CODER）教育和推广项目为K-12教师和学生教授数据科学概念。PI将使用传统（贝叶斯）和机器学习算法建立暗物质子结构形态分类的基线。他们将使用代表性暗物质模型库来训练机器学习算法进行异常检测，分类和属性估计。无监督异常检测技术将以独立于模型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和斯巴鲁望远镜上的Hyper Suprime-Cam的引力透镜数据，准备将他们的方法应用于Vera C的调查数据。鲁宾天文台。该奖项反映了NSF的法定使命，并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，被认为值得支持。