Conveners
Cosmology & Simulations: I
- Sebastian Trujillo
Recent advances in deep learning are triggering a revolution across fields. In this talk, I will discuss how we can apply these techniques to tackle complex problems in cosmology and astrophysics. I will first review how we can improve our understanding of fundamental physics by constraining the value of the cosmological parameters with the highest accuracy. After reviewing the standard...
As more and more data from recent and upcoming large cosmological surveys become available, the need for equally detailed theoretical models of galaxy formation, including large-volume cosmological simulations emerges. The complexity of such simulations, however, implies a great computational cost, which to this day always leads to a compromise in either resolution or size. As a supplement to...
Artificial neural networks are powerful machine learning models that can be trained to learn non-linear behaviors from data. In this talk, we present a new promising methodology for separating the CMB signal from foregrounds in Planck realistic simulations in temperature and polarization (formed by the CMB, Synchrotron and dust Galactic emissions, PS and thermal SZ extragalactic emissions and...
Galaxy formation is fueled by inflowing gas from the cosmic web. While on cosmological scales the gas distribution traces the dark matter, the relation between gas abundances and matter decouples at galactic scales and transitions into a highly non-linear regime due to the complex interplay of various astrophysical processes.
Hydrodynamical simulations are currently the most accurate tool...
