Helen Meskhidze (University of California, Irvine)
The increasing precision of cosmological observations of the large-scale structure of the universe has created a problem for simulators: running the N-body simulations necessary to interpret these observations has become impractical. Simulators have thus turned to machine learning (ML) algorithms instead. Though ML decreases computational expense, one might be worried about the use of ML for scientific investigations: How can algorithms that have repeatedly been described as black-boxes deliver scientific understanding? In this talk, I investigate how cosmologists employ ML, arguing that in this context, ML algorithms should not be considered black-boxes and can deliver genuine scientific understanding.
