Coherent Multi-stage Data Analyses in Astronomy and Solar Physics

Today’s sophisticated telescopes are providing massive new surveys containing terabytes of data, including high resolution spectrography and imaging across the electromagnetic spectrum. Dramatic increases in the quality and quantity of astrophysical data have enabled explicit detailed modelling of the physical processes that power complex astrophysical objects. This requires combining multiple models and data sources into an omnibus analysis, where outputs from one analysis are the inputs of subsequent analyses. In/Outputs may be high-dimensional with difficult-to-quantify correlations.

Researchers involved in such a chain of analyses may work quite independently with different research groups having different areas of expertise and different levels of statistical sophistication. Typically, this involves a combination of data-driven methods that are agnostic to the underlying physics with model-based methods that are tailored to specific scientific questions.

In this talk we explore how this plays out in the context of several examples taken from astrophysics, ranging from studying the expansion history of the universe, to mapping the physical characteristics of the solar corona. We will discuss how analyses based on pragmatic approximations compare with fully-Bayesian analyses, all with the goal of obtaining a coherent overall statistical analysis with reliable quantification of uncertainty.