Team overview page › Details for: Lukas Eisert
Fields of Interest:
Short summary of current research:
One of the main predictions of $\Lambda$CDM-cosmology is the hierarchical buildup of structure and therefore also the successive merging of galaxies to more massive ones. As one can only observe galaxies at one specific time in cosmic history, this merging history remains in principle unobservable. By using the well-resolved and large-statistics galaxy populations simulated within the IllustrisTNG project (https://www.tng-project.org), I want to show how it is possible to infer the unobservable stellar assembly/merging history of galaxies from observable properties of the main galaxy body and the topology of the faint stellar halo surrounding it. While it is difficult to quantify by hand the complex connections between observables and assembly histories, I am using cutting-edge machine learning techniques to model these. This allows not only to better understand and visualize those relationships, but also to ultimately transfer the learned knowledge to observational data from current and future galaxy surveys.