CAREER: Gravitational Wave Discovery Space

我们从天文学史中了解到，当打开一扇观察我们宇宙的新窗口时，我们要预料到意想不到的事情。为了最大限度地发挥引力波天文学新领域的发现潜力，我们需要在识别和表征引力波信号的数据分析方法方面取得进展。这个项目支持LIGO、NANOGrav和LISA合作的引力波数据分析关键领域的研究，同时培训在引力波天文学研究方法和科学交流技能方面通常在物理和天文学领域代表性不足的本科生。这个项目的研究目标是开发用于LIGO、NANOGrav和LISA天体物理搜索的贝叶斯数据分析方法。通过协调和组合整个引力波频谱的分析，这个项目将完成超出任何单个频段单独完成的科学。LIGO：开发对来自宇宙弦尖端的引力波信号的贝叶斯搜索、探测器特性和仪器噪声研究，包括优化地面引力波观测站全网络的数据分析，以及探测和表征意外引力波信号的综合数据分析方法。NANOGrav：将为LIGO数据分析开发的贝叶斯分析方法应用于NANOGrav搜索，包括噪声和信号建模。LISA：应用贝叶斯分析方法优化LISA任务的科学能力和发现潜力，包括极端质量比螺旋(EMRI)信号。PI为一个以本科为主的机构的学生提供机会，这些学生传统上在物理学和天文学方面的代表性不足，可以从事前沿科学研究和推广计划。本科生潜在研究项目的广度对教育体验至关重要，跨学科研究团队通过同行指导和学生领导角色得到支持。学生的教育经验将被评估，以告知和评估本科生的研究和外展经验。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。

We have learned from the history of astronomy to expect the unexpected when opening a new window with which to observe our universe. In order to maximize the discovery potential for the new field of gravitational wave astronomy, we need to make progress in our data analysis methods to identify and characterize gravitational wave signals. This project supports research in critical areas of gravitational wave data analysis for the LIGO, NANOGrav, and LISA collaborations while training undergraduate students who are typically underrepresented in the fields of physics and astronomy in gravitational wave astronomy research methods and science communication skills.The research objectives for this project are to develop Bayesian data analysis methods for astrophysical searches for LIGO, NANOGrav, and LISA. By coordinating and combining analyses across the full gravitational wave spectrum, ranging over 12 decades in frequency, this project will accomplish science beyond what could be done in any individual band alone. LIGO: development of a Bayesian search for gravitational wave signals from cosmic string cusps, detector characterization and instrument noise studies including optimization of data analysis for the full network of ground based gravitational wave observatories, and a comprehensive data analysis method for detecting and characterizing unexpected gravitational wave signals. NANOGrav: apply Bayesian analysis methods developed for LIGO data analysis to NANOGrav searches including noise and signal modeling. LISA: application of Bayesian analysis methods to optimize the science capabilities and discovery potential for the LISA mission including Extreme Mass Ratio Inspiral (EMRI) signals. The PI provides opportunities for students at a primarily undergraduate institution who are traditionally underrepresented in physics and astronomy to engage in forefront scientific research and outreach programs. The breadth of potential research projects for undergraduate students is critical for the educational experience, with interdisciplinary research teams supported through peer mentoring and student leadership roles. Student educational experiences will be assessed to inform and evaluate the undergraduate research and outreach experiences.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.