Title: Data-driven and science-driven Bayesian methods in astronomy and solar physics
Authors: David van Dyk - Imperial College London (United Kingdom) [presenting]
Abstract: In recent years, technological advances have dramatically increased the quality and quantity of data available to astronomers. Newly launched or soon-to-be launched space-based telescopes are tailored to data-collection challenges associated with specific scientific goals. These instruments provide massive new surveys resulting in new catalogs containing terabytes of data, high resolution spectrography and imaging across the electromagnetic spectrum, and incredibly detailed movies of dynamic and explosive processes in the solar atmosphere. The spectrum of new instruments is helping scientists make impressive strides in our understanding of the physical universe, but at the same time generating massive data-analytic and data-mining challenges for scientists who study the resulting data. We will illustrate and discuss the interplay of data science, machine learning, Bayesian statistics, as well as data-driven and science-driven methods in the context of several problems in astrophysics, ranging from studying the expansion history of the universe, to disentangling overlapping sources, and mapping the physical characteristics of the solar corona. A common theme involves strategies for combining multiple data sets analysed sequentially into a single coherent statistical result.