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Harnessing Machine Learning to Study the Life Cycle of Stars

利用机器学习研究恒星的生命周期

基本信息

批准号：
1812747
负责人：
Stella Offner
金额：
$ 37.03万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1812747&HistoricalAwards=false
关键词：
Harnessing Machine Learning Study Life

项目摘要

Stars like our Sun are born in large clouds of gas that usually produce thousands of stars at once. As the stars form, they add energy back into their environment ('feedback') and influence the birth gas cloud. This feedback appears as fast-moving gas, which sometimes looks like large bubbles. The data are complex, so identifying feedback is difficult. Typically, astronomers have found the feedback "by eye", which is subjective and time-consuming. A new field of computer science, machine learning, provides an alternative approach to find feedback. In machine learning, computer algorithms are trained to identify features in the same way the human brain recognizes objects - like cats, dogs and cars. The investigator's group will use state-of-the-art models of forming stars to train machine learning algorithms to find feedback. They will apply the algorithms to telescope observations and compare with the feedback sources previously found by humans. The investigator will share the models with the public through the Milky Way project, which is an online astronomy program that trains people to identify feedback in telescope images of gas clouds. The program will also train students in research techniques, including undergraduates from underrepresented groups. The proposal addresses a fundamental star formation question: How much mass and energy is associated with stellar feedback in the interstellar medium? To answer this question, the PI and collaborators will use magnetohydrodynamic simulations of forming stars, for which full feedback information is known, to train machine learning algorithms to identify and quantify feedback. Dust and molecular line 'synthetic observations' will be produced and used together with observational data as a training set. The investigators will compare the machine learning identifications to prior visually identified feedback catalogs, including those from the citizen-science Milky Way Project, create an updated census, and publicly release the algorithm and data to the community. The broader impact objectives are to increase public participation in the Milky Way Project, develop a WorldWide Telescope tour on feedback, and train students, including undergraduates in the Texas Astronomy Undergraduate Research experience for Under-Represented Students (TAURUS) summer program.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.

像太阳这样的恒星诞生于巨大的气体云中，通常会同时产生数千颗恒星。当恒星形成时，它们会向环境中添加能量（“反馈”）并影响诞生气体云。这种反馈表现为快速移动的气体，有时看起来像大气泡。数据很复杂，因此识别反馈很困难。通常，天文学家“通过眼睛”发现反馈，这是主观且耗时的。计算机科学的一个新领域——机器学习，提供了另一种寻找反馈的方法。在机器学习中，计算机算法被训练来识别特征，就像人脑识别物体（如猫、狗和汽车）一样。研究小组将使用最先进的恒星形成模型来训练机器学习算法来寻找反馈。他们将把这些算法应用到望远镜观测中，并与人类之前发现的反馈源进行比较。研究人员将通过银河系项目与公众分享这些模型，这是一个在线天文学项目，训练人们识别气体云望远镜图像中的反馈。该计划还将培训学生的研究技术，包括来自代表性不足群体的本科生。该提案解决了一个基本的恒星形成问题：有多少质量和能量与星际介质中的恒星反馈相关？为了回答这个问题，首席研究员和合作者将使用形成恒星的磁流体动力学模拟（已知完整的反馈信息）来训练机器学习算法来识别和量化反馈。灰尘和分子线“综合观测”将被生成并与观测数据一起用作训练集。研究人员将把机器学习识别与之前视觉识别的反馈目录（包括来自公民科学银河计划的反馈目录）进行比较，创建更新的人口普查，并向社区公开发布算法和数据。更广泛的影响目标是增加公众对银河系项目的参与，开发全球望远镜反馈之旅，并培训学生，包括本科生参加德克萨斯州天文学本科生研究经验不足学生 (TAURUS) 夏季项目。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。