Daniela Huppenkothen from NYU will be presenting this Friday, 5/19 in Z-679 at the computational statistics club. The title of her talk is "Timing Black Holes: Time Series Analysis in High-Energy Astronomy". This will be an interesting discussion on time series analysis in a data limiting regime!
"Timing Black Holes: Time Series Analysis in High-Energy Astronomy"
The sky in X-rays is incredibly dynamic. Black holes vary on time scales ranging from milli-seconds to decades, their brightness occasionally changing by several orders of magnitude within seconds or minutes. Studying this variability is one of the best ways to understand key physical processes that are unobservable on Earth: general relativity in strong gravity, extremely dense matter and the strongest magnetic fields known to us are just a few examples.
However, astronomical time series can be difficult to analyze in practice: many of the time series are inherently non-stationary, observing constraints lead to a very uneven sampling, and the underlying process is often partly stochastic in nature. Furthermore, classification problems are complicated by the fact that we are very limited by our relatively small, imbalanced data sets.
In this talk, I will give an overview of the state-of-the-art of time series analysis in high-energy astronomy. I will present key statistical methods and machine learning models we have been developing recently as well as point out opportunities and the many challenges of the spectral-timing revolution we are moving toward with data from current and future space missions.