Abstract
The volume and variety of data in astrophysics creates a need for efficient heuristics to automate the discovery of novel phenomena. Moreover, data-driven practices suggest a role for machine-led exploration in conceptual development. I argue that philosophical accounts of exploratory experiments should be amended to include the characteristics of cases involving machine learning, such as the use of automation to vary experimental parameters and the prevalence of idealized and abstracted representations of data. I consider a case study that applies machine learning to develop a novel galaxy classification scheme from a dataset of ‘low-level’ but idealized observables.
