MathStatBites at SCMA8: Astro Image Processing is BLISS?
In June 2023, astronomers and statisticans flocked to “Happy Valley’” Pennsylvania for the eighth installment of the Statistical Challenges in Modern Astronomy, a bidecadal conference. The meeting, hosted at Penn State University, marked a transition in leadership from founding members Eric Feigelson and Jogesh Babu to Hyungsuk Tak, who led the proceedings. While the astronomical applications varied widely, including modeling stars, galaxies, supernovae, X-ray observations, and gravitational waves, the methods displayed a strong Bayesian bent. Simulation based inference (SBI), which uses synthetic models to learn an approximate function for the likelihood of physical parameters given data, featured prominently in the distribution of talk topics. This article features work presented in two back-to-back talks on a probabilistic method for modeling (point) sources of light in astronomical images, for example stars or galaxies, delivered by Prof. Jeffrey Regier and Ismael Mendoza from the University of Michigan-Ann Arbor.
Bridging the Gap between Models and Data
One of the key goals of science is to create theoretical models that are useful at describing the world we see around us. However, no model is perfect. The inability of models to replicate observations is often called the “synthetic gap.” For example, it may be too computationally expensive to include a known effect or to vary a large number of known parameters. Or, there may be unknown instrumental effects associated with variability in conditions during the data acquisition.