Statistical Methods for the Physical Sciences Seminar

  • Remote Access - Zoom
  • Virtual Presentation - ET
  • DANIELA HUPPENKOTHEN
  • Staff Scientist and NWO WISE Fellow
  • SRON Netherlands Institute for Space Research
  • and Data Science Fellow, eScience Institute, University of Washington
Seminars

Unravelling the Physics of Black Holes Using Astronomical Time Series

Black holes are at the heart of many open questions in astrophysics. They are prime laboratories to study the effects of strong gravity, and are thought to play a significant role in the evolution of the universe. Much of our knowledge of these sources comes from studies of black holes in X-ray binaries, where a black hole exists in a binary system with a star, and is observed through the radiation emitted by stellar material as it falls into the black hole. Of particular interest are their time series, measurements of their brightness as a function of time. Connecting properties of these (often stochastic) time series to physical models of how matter falls into black holes enables probes of fundamental physics, but requires sophisticated statistical methods commonly grouped under the term “spectral timing”. In addition, data analysis is often complicated by systematic biases introduced by the detectors used to gather the data.

Daniela Huppenkothen is a staff scientist at the SRON Netherlands Institute for Space Research. Previously, she was Associate Director of the Center for Data-Intensive Research in Astrophysics and Cosmology (DIRAC) at the University of Washington. Before that, she spent time at New York University as a Moore-Sloan Data Science Postdoctoral Fellow, after receiving her PhD at the University of Amsterdam in Astronomy in 2014. Daniela is interested in leveraging new statistical and computational methods to improve inference within astronomy and space science. Her current research focuses mostly on time series analysis across all parts of astronomy, including asteroids, neutron stars and black holes. She is interested in how we can use machine learning and statistics to mitigate biases introduced into our data by detectors and telescopes. She is lead developer of the open-source software project Stingray, which implements a collection of commonly used time series methods in astronomy. She is interested in finding new ways to teach data science to astronomers (often with candy), and she develops new strategies for facilitating interdisciplinary collaborations in her role as co-organizer of Astro Hack Week.

Faculty Hosts: Mikael Kuusela, Ann Lee, Larry Wasserman

Zoom Participation. See announcement.

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