Big Data and Planets

Astronomy is in the midst of a transformation brought on by exponentially progressing technological advances in the information age. New detector capabilities and faster computation have created a new era in which the use of advanced data mining and inference methods could bring new answers to long-standing scientific questions. The proposed research group, which includes leading figures in data analysis of exo-planets will

• prepare algorithms for analysis of data from the forthcoming TESS space mission,

• apply Gaussian Processes and machine learning algorithms to model stellar variability in transit and radial-velocity studies of exo-planets, and

• study exo-planetary system architectures by developing population models and confront them with the accumulating data, using new statistical tools.

We expect the research group to provide a better understanding of the exo-planetary population via advanced statistical tools — a giant leap in one of the most exciting fields of present science.

Research Group Members

Related Events

Period of Residence

May 1, 2019

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July 31, 2019

Discipline

Field of Research

Exoplanetary Science, Astrostatistics, and Machine Learning in Astronomy

Research Question

How can advanced statistical methods and machine learning algorithms improve the analysis of exoplanetary data—particularly from missions like TESS—and what new insights can they offer into the architectures and variability of exoplanetary systems?