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.

像我们的太阳这样的恒星诞生于大云的气体中，通常一次产生数千颗恒星。随着恒星形成，它们将能量恢复到环境中（“反馈”）并影响出生气云。这种反馈看起来像是快速移动的气体，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，天文学家发现了“眼睛”的反馈，这是主观且耗时的。一个新的计算机科学领域，机器学习，提供了一种寻找反馈的替代方法。在机器学习中，对计算机算法进行了训练，以识别人脑识别物体（例如猫，狗和汽车）的方式。研究者的小组将使用形成恒星的最先进模型来训练机器学习算法以查找反馈。他们将把算法应用于望远镜观察结果，并与人类先前发现的反馈来源进行比较。研究人员将通过银河系项目与公众共享模型，这是一项在线天文学计划，训练人们在气云的望远镜图像中识别反馈。该计划还将培训学生的研究技术，包括来自代表性不足的小组的本科生。该提案解决了一个基本的星形构成问题：星际介质中的质量和能量与恒星反馈有关？为了回答这个问题，PI和合作者将使用磁性水力动力学模拟形成的星星，为此，已知全部反馈信息，以训练机器学习算法来识别和量化反馈。灰尘和分子线“合成观测”将被产生并与观测数据一起作为训练集。调查人员将将机器学习识别与先前的视觉标识的反馈目录进行比较，包括来自公民科学银河系项目的反馈目录，创建更新的人口普查，并将算法和数据公开发布给社区。更广泛的影响目标是增加公众参与银河系项目，开发全球望远镜巡回赛，并培训学生，包括德克萨斯州天文学本科生的本科生研究经验（Taurus）学生（Taurus）夏季计划的本科生研究经验。这一奖项反映了NSF的法定任务和经过评估的范围。