Discovery and Dynamical Classification of Accreting Compact Objects with the Zwicky Transient Facility

丽贝卡·菲利普森被授予数学和物理科学提升博士后研究奖学金（MPS-Ascend），在华盛顿大学进行研究和教育项目。菲利普森博士计划利用即将到来的大规模光学研究，比如美国国家科学基金会的维拉·c·鲁宾天文台时空遗产调查。她证明了非线性动力学递归分析方法能够区分x射线双星和活动星系核中的确定性、随机和混沌信号。菲利普森计划从几个方面扩大人们对天体物理学领域的参与。她将指导参加天文学预专业项目的学生，该项目为来自代表性不足群体的学生提供了进行前沿研究的机会。Phillipson的初步工作表明，对恒星光曲线的综合研究，结合递归方法和机器学习，将基于它们的动态可变性对可变源进行新的分类。数值方法为准周期变量目标的检测开辟了一个新的发现空间。她将与eScience研究所合作，开发一个可访问的网站，以传播她的研究项目的科学成果。她的数据访问计划的一个关键特点是使视障科学家能够通过使用声音来促进特征检测和分析，从而与数据进行交互。这种方法被称为声波化，使用现有的软件工具将科学数据映射到声学序列。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

Rebecca Phillipson is awarded a Mathematical and Physical Sciences Ascending Postdoctoral Research Fellowship (MPS-Ascend) to conduct a program of research and education at Washington University. Dr. Phillipson plans to take advantage of upcoming large scale optical studies, like the NSF’s Vera C. Rubin Observatory Legacy Survey of Space and Time. She has demonstrated recurrence analysis methods from nonlinear dynamics are capable of distinguishing between deterministic, stochastic, and chaotic signals in X-ray Binary Stars and Active Galactic Nuclei. Dr. Phillipson plans to broaden participation in the astrophysics field in several ways. She will mentor students participating in the Pre-Major in Astronomy program which gives opportunities for students from underrepresented groups to perform cutting edge research.Phillipson’s preliminary work suggests that an ensemble study of stellar light curves, with the recurrence methods, paired with machine learning, will result in novel classifications of variable sources based on their dynamical variability. The numerical approaches open a new discovery space for detection of quasi-periodic variable objects. She will partner with the eScience Institute to develop an accessible website for the dissemination of the scientific results from her research program. A key feature of her data access plan is enabling visually impaired scientists to interact with the data by using sounds to facilitate feature detection and analysis. This approach, called sonification, uses existing software tools to map scientific data to acoustic sequences.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.