The Importance of Being Interpretable: Toward An Understandable Machine Learning Encoder for Galaxy Cluster Cosmology

Cosmology is entering an era of data-driven science, due in part to modern machine learning techniques that enable powerful new data analysis methods. This is a shift in our scientific approach, and requires us to ask an important question: Can we trust the black box? In this talk, I will describe methods for building trust in machine learning models, focusing on models for interpreting cosmological large scale structure. I will show examples of how machine learning can be used, not just as a tool for getting “better” results at the expense of understanding, but as a partner that can point us toward physical discovery.