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In order to prepare for the upcoming wide-field cosmological surveys, largesimulations of the Universe with realistic galaxy populations are required. Inparticular, the tendency of galaxies to naturally align towards overdensities,an effect called intrinsic alignments (IA), can be a major source ofsystematics in the weak lensing analysis. As the details of galaxy formationand 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 onthe IllustrisTNG-100 simulation and is capable of sampling the orientations ofa population of galaxies so as to recover the correct alignments. In ourapproach, we model the cosmic web as a set of graphs, where the graphs areconstructed for each halo, and galaxy orientations as a signal on those graphs.The generative model is implemented on a Generative Adversarial Networkarchitecture and uses specifically designed Graph-Convolutional Networkssensitive to the relative 3D positions of the vertices. Given (sub)halo massesand tidal fields, the model is able to learn and predict scalar features suchas galaxy and dark matter subhalo shapes; and more importantly, vector featuressuch as the 3D orientation of the major axis of the ellipsoid and the complex2D ellipticities. For correlations of 3D orientations the model is in goodquantitative agreement with the measured values from the simulation, except forat very small and transition scales. For correlations of 2D ellipticities, themodel is in good quantitative agreement with the measured values from thesimulation on all scales. Additionally, the model is able to capture thedependence of IA on mass, morphological type and central/satellite type.

Galaxies and Halos on Graph Neural Networks: Deep Generative Modeling Scalar and Vector Quantities for Intrinsic Alignment