Calibrating Quasars for Cosmology

宇宙学可以被描述为研究宇宙的历史，未来，结构和几何形状，是天文学研究的基础。 天文学家目前对宇宙学的最好理解来自于使用某种类型的爆炸恒星（超新星）来“绘制”宇宙。 然而，科学的一个基本原则是，结果应该用独立的方法进行测试和确认。 因此，这项研究旨在确定是否可以使用另一种类型的天体来测试当前的理论。 这些物体是潜伏在所有大质量星系核心的黑洞，其中一些今天正在积极“进食”，被称为“类星体”。 最终的目标是试图确定类星体的一个子类是否可以像超新星一样用来绘制宇宙的几何形状。 在这个过程中，研究生将接受现代数据科学方法的培训，学生和PI都将通过涉及德雷克塞尔林奇天文台的活动，与小学和中学课堂接触，并积极致力于提高德雷克塞尔大学和科学界的包容性和多样性。该项目具有推进科学的潜力，因为已知的类星体类星体的数量远远超过了目前作为宇宙学基础的超新星，类星体的"寿命"更长，而且被发现的时间跨越了宇宙时间。 人们可能希望从它们那里学到的东西将对现有方法提供独立的限制。 该项目将采用一种新的方法，利用机器学习来改进和扩展类星体在宇宙学中的应用。 这项工作涉及校准两个关系，可以应用于多达50万个已知的类星体，并可以使用斯隆数字巡天的现有公共数据来完成。 该项目被设计为一项为期一年的高风险、高潜在回报的探索性调查，提案团队具有独特的优势。这项研究的结果将广泛传播，不仅通过科学文献，而且通过共同的数据共享平台和外展活动提供代码和数据产品。该奖项反映了NSF的法定使命，并被认为值得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来支持。

Cosmology can be described as the study of the history, future, structure, and geometry of the Universe and is a foundation of astronomical research. Astronomers' current best understanding of cosmology comes from using a certain type of exploding stars(supernovae) to "map" the Universe. However, a fundamental tenet of science is that results should be tested and confirmed with independent methods. This investigation thus seeks to determine if another type of astronomical object can be used to test current theories. These objects are black holes that lurk in the cores of all massive galaxies, some of which are actively "feeding" today and are called "quasars". Ultimately the goal is to attempt to determine if a sub-class of quasars can be used in the same way as supernovae to map the geometry of the universe. In this process a graduate student will be trained in modern data science methods and both the student and PI will support education and public outreach, through activities involving Drexel's Lynch Observatory, engaging with elementary and middle-school classrooms, and actively working to increase inclusion and diversity at both Drexel University and in the science community in general.This project has potential for advancing science because known quasars vastly outnumber the supernovae that are currently the foundation of cosmology and the quasars "live" longer in addition to having been discovered across cosmic time. What one might hope to learn from them would provide independent constraints on existing methods. This project will undertake a novel method for leveraging machine learning to improve and expand upon the use of quasars for cosmology. The work involves calibrating two relationships that can be applied to up to 500,000 known quasars and can be accomplished using existing public data from the Sloan Digital Sky Survey. The project is designed as a 1-year high-risk, high potential reward exploratory investigation that the proposing team is uniquely positioned to carry out. The results of this investigation will be broadly distributed, by making code and data products available not only through the science literature, but also common data-sharing platforms and outreach activities.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.