How to Time a Black Hole: Time series Analysis for the Multi-Wavelength Future

Abstract:

Virtually all astronomical sources are variable on some time scale, making studies of variability across different wavelengths a major tool in pinning down the underlying physical processes. This is especially true for accretion onto compact objects such as black holes: “spectral-timing”, the simultaneous use of temporal and spectral information, has emerged as the key probe into strong gravity and accretion physics. The new telescopes currently starting operations or coming online in the coming years, including the Square Kilometre Array (SKA), the Large Synoptic Survey Telescope (LSST) and the Cherenkov Telescope Array (CTA), will open up the sky to transient searches, monitoring campaigns and time series studies with an unprecedented coverage and resolution. But at the same time, they collect extraordinarily large data sets of previously unknown complexity, motivating the necessity for new tools and statistical methods. In this talk, I will review the state-of-the-art of astronomical time series analysis, and discuss how recent developments in machine learning and statistics can help us study both black holes and other sources in ever greater detail. I will show possible future directions of research that will help us address the flood of multiwavelength time series data to come.

Bio:

Daniela Huppenkothen received a Bachelor Degree in Geosciences and Astrophysics from the Jacobs University in Bremen in 2008 and the M.Sc. and Ph.D. degrees from the University of Amsterdam in Astronomy and Astrophysics in 2010 and 2014 respectively. Since October 2016 she works as an James Arthur Postdoctoral Fellow at the New York University. Her interests are time series analysis in astronomy, astrostatistics, X-ray data analysis, and machine learning.