- Sept. 16, 2019, 2:00 pm US/Central
- Curia II
- Luisa Lucie-Smith, University College London
Dark matter halos are the fundamental building blocks of cosmic large-scale structure. Improving our theoretical understanding of their structure, evolution and formation is an essential step towards understanding how galaxies form. Although N-body simulations are the only tool to fully compute the non-linear gravitational evolution of halos, it is difficult to gain physical interpretation from numerical studies alone. I will present a machine learning approach which aims to provide new physical insights into the physics driving halo formation. We train a machine learning algorithm to learn the relationship between the initial conditions and the final dark matter halos directly from N-body simulations. We evaluate the predictive performance of the algorithm when provided with different types of information about the initial conditions, allowing us to infer which aspects of the early-Universe density field impact the formation of the final dark matter halos. In general, our approach can be extended to yield physical understanding of other complex non-linear processes in the context of cosmological structure formation and beyond.