Luisa Lucie-Smith awarded RAS Michael Penston Thesis Prize - runner up
In her PhD, Luisa Lucie-Smith developed a new approach based on machine learning, aimed at deepening our understanding of cosmological structure formation in the Universe. A theoretical understanding of the structure, evolution and formation of dark matter haloes is an essential step towards unravelling the intricate connection between halo and galaxy formation, needed to test our cosmological model against data from upcoming galaxy surveys.
Luisa and here colleagues trained machine learning algorithms to learn the relationship between the initial conditions and the final dark matter halos in N-body simulations. Their goal was to understand what information is learnt by the machine learning algorithm about the underlying connection between the early universe and the late-time dark matter haloes; this differs from common approaches where machine learning is utilized as a black-box tool to obtain fast and automated mappings. The new method led to a re-interpretation of the existing understanding of halo formation over the last decades, in particular in relation to the role of the tidal shear tensor in establishing the final mass of dark matter haloes.
Since November 2020, Luisa is a research fellow at the Max Planck Institute for Astrophysics in Garching, working on her own research program to make further developments on interpretable machine learning tools applied to cosmological structure formation. She is particularly interested in understanding the structure of dark matter halos and their relation to the initial conditions of the Universe, as well as the connection between halos and galaxies. As the next step, she plans to apply these techniques to other less-understood cosmic structures such as voids.