Speaker
Description
Although the LambdaCDM model can explain large-scale cosmological structures very well, some small-scale observations still do not match CDM predictions. To explain the discrepancy between theory and observations, warm dark matter (WDM) has been proposed as a possible explanation. Motivated by this idea, the DREAMS simulation suite is designed to explore the impact of WDM on galaxy formation. However, running simulations with different WDM models, along with variations in baryonic physics, is computationally expensive. Emulators are a useful tool for addressing this problem. An emulator uses existing simulations with different initial conditions and combines them with machine learning techniques to explore the parameter space and predict simulation outcomes. In this work, we use the DREAMS WDM Milky Way zoom-in simulations to build an emulator and explore how WDM and baryonic physics affect the mass, metallicity, and number of satellites of Milky Way–like galaxies. In addition, motivated by upcoming observations, we also analyze photometric measurements from mock images generated with ARRAKIHS filters to investigate how future deep photometric observations can improve our understanding of galaxy formation and assembly history.