Speaker
Description
In order to prepare for the upcoming wide-field cosmological surveys, large simulations of the
Universe with realistic galaxy populations are required. In particular, the tendency of galaxies
to naturally align towards overdensities, an effect called intrinsic alignments (IA), can be a
major source of systematics in the weak lensing analysis. As the details of galaxy formation
and evolution relevant to IA cannot be simulated in practice on such volumes, we propose as an
alternative a Deep Generative Model. This model is trained on the IllustrisTNG-100 simulation
and is capable of sampling the orientations of a population of galaxies so as to recover the
correct alignments. In our approach, we model the cosmic web as a set of graphs, where the
graphs are constructed for each halo, and galaxy orientations as a signal on those graphs. The
generative model is implemented on a Generative Adversarial Network architecture and uses
specifically designed Graph-Convolutional Networks sensitive to the relative 3D positions of
the vertices. Given (sub)halo masses and tidal fields, the model is able to learn and predict
scalar features such as galaxy shapes; and more importantly, vector features such as the 3D
orientation of the major axis of the ellipsoid and the complex 2D ellipticities. For correlations
of 3D orientations the model is in good quantitative agreement with the measured values from
the simulation, except for at very small and transition scales. For correlations of 2D ellipticities,
the model is in good quantitative agreement with the measured values from the simulation.
