Physics Graduate Student Symposium (PGSS) | Machine Learning in Cosmology

In the upcoming decades, we will have the opportunity to solve some of the biggest questions about our universe by taking advantage of the huge amounts of data produced by upcoming state-of-the-art cosmological experiments. In order to harness the full statistical power of this data, we will need to develop scalable and accurate algorithms that can extract its maximal information. Recent advances in Machine Learning have demonstrated its ability to overcome the computational bottlenecks of traditional statistical techniques and even achieve better performance when analyzing cosmology data. In this talk, I will give a brief overview of the open problems in cosmology, motivate how Machine Learning (ML) could help us answer these by enabling novel analyses of upcoming cosmological surveys, and give a specific application of ML enabling probabilistic detection and measurement of galaxy images.