From Asteroids to Black Holes: Data Science for Next-Generation Time Domain Astronomy

Across almost all scientific disciplines, the instruments that record our experimental data and the methods required for storage and data analysis are becoming increasingly sophisticated. This has been particularly true for astronomy, where current and future instruments produce data sets of a size and complexity that are difficult to make sense of with traditional methods. In this talk, I will focus on recent research on time series in astronomy and present examples of how we can use modern statistical and machine learning methods to help us explore and understand the physical processes underlying a diverse range of phenomena, from the composition and shape of asteroids to the physics of matter falling into black holes. I will show how in the future, these new methods and tools will help us make sense of data sets across all wavelengths, for example from large astronomical surveys like the upcoming Large Synoptic Survey Telescope.

The universal applicability of data science tools to a broad range of problems has also generated new opportunities to foster exchange of ideas and computational workflows across disciplines. I will discuss ways to enable interdisciplinary collaboration in order to solve data analysis problems across multiple domains.