Astrophysics

Deviations from a parity-symmetric Universe would strongly signal the presence of new physics beyond the Standard Model. The lowest-order scalar observable sensitive to parity in a homogeneous and isotropic Universe is the connected four-point function of a given cosmological field. Geometrically, this function is described by a suitable average of this field over many tetrahedral configurations. As the CMB represents a spherical projection of three-dimensional curvature fluctuations, its four-point function serves as a unique, but potentially limited, probe of this fundamental symmetry. In this talk, I will discuss progress in rigorously understanding the CMB’s sensitivity to parity-violating physics from both early- and late-time sources.

We are entering the golden age of multi-wavelength astronomical surveys. In the 2020s, a plethora of surveys (such as Euclid, eROSITA, Rubin-LSST, Simons Observatory, and CMB-S4) are underway or planned to provide unprecedented insights into cosmology and astrophysics. In this talk, I will discuss the significant scientific opportunities and challenges that arise in the era of big data, highlighting recent advances in computational modeling and the roles of artificial intelligence and machine learning.

Galaxies are the medium through which we study the structure of the universe. However, widely applied statistical models of galaxies are generally over-simplified: even recently proposed models cannot capture the dependencies on environment or formation history. To solve this problem, I will introduce Graph Neural Networks (GNNs), a general and ideal tool for physical modelling. Geometrically constrained GNNs vastly improve our models, and allow us to ask detailed questions about the importance of formation history and environment for cosmological galaxy modeling. I will also prove a surprising equivalence between these two aspects of galaxy formation.