CAREER: Building the Merger Tree of the Milky Way with Machine Learning

首席调查员(PI)Necib和她的团队将在我们的银河系中搜索恒星碎片。这些恒星结构是星系与银河系相撞时形成的。该团队将利用人工智能开发一个自动化过程，以完成欧洲航天局(ESA)任务GAIA对15亿多颗恒星的观测数据进行分析的艰巨任务。研究小组将把他们的数据与模拟进行比较，以绘制银河系形成的历史。国际和平协会致力于扩大参与。在她成功的突尼斯播客Ga3da Falaki的基础上，PI将与她的研究生合作，制作一系列多种语言的播客。PI还将为本科生和高中生举办一系列盖亚黑客松。PI的目标是使用监督和非监督机器学习技术来构建银河系的合并树。PI将构建一个将测量不确定性和尺度随数据大小的增加整合在一起的聚类算法，以及一个可解释的等变图神经网络，预测合并到银河系的合并时间和合并质量，在Illustris-TNG50上进行训练，并应用于Gaia DR3/4数据。该奖项反映了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。

Principal Investigator (PI) Necib and her team will search for stellar debris in our galaxy. These stellar structures were formed when galaxies collided with the Milky Way. The team will use artificial intelligence to develop an automated process to complete the difficult task of analyzing observations of more than 1.5 billion stars from the European Space Agency (ESA) mission Gaia. The team will compare their data to simulations to chart the history of the formation of the Milky Way. The PI is committed to broadening participation. Building on her successful Tunisian Podcast Ga3da Falakia, the PI will work with her graduate students to produce a series of podcasts in a range of languages. The PI will also host a series of Gaia Hackathons for undergraduate and high school students. The PI aims to build the merger tree of the Milky Way using both supervised and unsupervised machine learning techniques. The PI will build a clustering algorithm that integrates measurement uncertainties and scales with the increase in data size, as well as an interpretable equivariant graph neural network that predicts the merging times and infall masses of mergers into the Milky Way, trained on Illustris-TNG50, and applied to Gaia DR3/4 data.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.