Graduate school seminar: Kseniia Sysoliatina - Understanding the evolution of the Milky Way with the semi-analytic models

Speaker: Kseniia Sysoliatina, Leibniz Institute for Astrophysics Potsdam, Germany

Title: Understanding the evolution of the Milky Way with the semi-analytic models

Abstract

Over the past several decades, our understanding of the processes driving the formation and evolution of galaxies has improved dramatically – largely thanks to such high- resolution numerical simulations as IllustrisTNG that model gravitational interactions, gas flows, and the complex interplay of star formation and feedback. However, such simulations remain extremely resource-intensive, making comprehensive exploration of parameter space challenging. Moreover, while they can reproduce Milky Way-like galaxies in a statistical sense, they cannot yet model our Galaxy in detail.

Semi-analytic modeling offers a powerful and efficient alternative approach, enabling the construction of a coherent and flexible picture of the Milky Way. In the era of extensive Galactic datasets such as Gaia, LAMOST, APOGEE, and the forthcoming 4MOST and LSST releases, developing robust and self-consistent models of the Galaxy has become more important than ever.

In this talk, I will introduce the theoretical foundations of Galactic modeling and highlight the key differences between numerical simulations and semi-analytic models (SAMs). I will review several of the most widely used SAMs, including the Besançon Galaxy Model, and demonstrate how their predictions can be used to constrain the Milky Way’s morphology, star formation and chemical enrichment histories, and the initial mass function. Finally, I will discuss the crucial role of understanding data selection functions, as well as potential biases and correlations between model parameters, when comparing synthetic stellar populations to observational data.