Collaborative Research: SLINGSHOT: Decoding Dark Matter through Gravitational Lensing

暗物质的影响可以在引力透镜系统中看到，在这个系统中，来自遥远星系的光被前景星系引力透镜。对产生的背景星系的扭曲、放大、多重图像的分析为了解暗物质的性质提供了线索。首席研究人员(PI)格雷泽、艾姆斯和亚历山大将开发强大的透镜推理和生成系统，以利用观测望远镜数据(Singshot)框架来模拟真实的引力透镜图像，并应用机器学习算法来识别可能的暗物质亚结构的已知类别。作为全国黑人物理学家协会计算物理倡议的一部分，PI将通过一系列关于机器学习和天体物理的在线讲座来扩大参与范围。该团队还将继续使用Jupyter笔记本电脑教授数据科学概念，通过他们针对K-12教师和学生的创造性开放数据环境(CODER)教育和推广项目。PI将建立使用传统(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。他们将使用具有代表性的暗物质模型库，在异常检测、分类和性质估计方面训练机器学习算法。无监督异常检测技术将以一种与模型无关的方式识别有前景的候选图像，并定位异常以供进一步分析。PI将使用该代码分析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.Rubin天文台的测量数据。弹弓工具包将向天文学社区提供。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。

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.