Shirley Ho, Ph.D.Group Leader, Flatiron Institute
Cosmology X Data Science, CCA
Simons Foundation Lectures are free public colloquia related to basic science and mathematics. These high-level talks are intended for professors, students, postdocs and business professionals, but interested people from the metropolitan area are welcome as well.
A full understanding of the evolution of the universe’s structure is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the universe and compare the findings to computer simulations. Simulating the movement of billions of particles over billions of years is a daunting task, however, even when using the simplest physical models.
In this lecture, Shirley Ho will discuss her team’s work building a deep neural network that learns from a set of pre-run numerical simulations and predicts the large scale structure of the universe. Extensive analysis demonstrates that their deep-learning technique outperforms the commonly used fast approximate simulation method in predicting cosmic structure in the non-linear regime. They also show that their method can accurately extrapolate far beyond its training data and predict structure formation for significantly different cosmological parameters. This ability to extrapolate outside its training set is highly unexpected and remains a mystery.