TSI Seminar

Studying the Universe with Astrostatistics

Gwen Eadie

University of Toronto

Astrostatistics is a growing interdisciplinary field at the interface of astronomy and statistics. Astronomy is a field rich with publicly available data, but inference using these data must acknowledge selection effects, measurement uncertainty, censoring, and missingness. In the Astrostatistics Research Team (ART) at the University of Toronto — a joint team between the David A. Dunlap Department of Astronomy & Astrophysics and the Department of Statistical Sciences — we take an interdisciplinary approach to analysing astronomical data from a range of objects such as stars, star clusters, and galaxies. In this talk, I will cover three ART projects that employ Bayesian inference techniques to: (1) find stellar flares in time series data from stars using hidden Markov models, (2) investigate the relationship between old star cluster populations and their host galaxies using hurdle models, and (3) discover potential “dark” galaxies within an inhomogeneous Poisson Process framework.