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Generative Adversarial Networks (GANs) are a class of artificial neural network that can produce realistic, but artificial, images that resemble those in a training set. In typical GAN architectures these images are small, but a variant…
Diffusion models are at the vanguard of generative AI research with renowned solutions such as ImageGen by Google Brain and DALL.E 3 by OpenAI. Nevertheless, the potential merits of diffusion models for communication engineering…
Large photometric surveys will image billions of galaxies, but we currently lack quick, reliable automated ways to infer their physical properties like morphology, stellar mass, and star formation rates. Simulations provide galaxy images…
Elliptical galaxies today appear aligned with the large-scale structure of the Universe, but it is still an open question when they acquire this alignment. Observational data is currently insufficient to provide constraints on the time…
Weak gravitational lensing has become a powerful probe of large-scale structure and cosmological parameters. Precision weak lensing measurements require an understanding of the intrinsic alignment of galaxy ellipticities, which can in turn…
Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in Artificial Intelligence (specifically Deep Learning) to address this problem. Neural networks have been…
We develop empirical methods for modeling the galaxy population and populating cosmological N-body simulations with mock galaxies according to the observed properties of galaxies in survey data. We use these techniques to produce a new set…
The intrinsic alignments of galaxies, i.e., the correlation between galaxy shapes and their environment, are a major source of contamination for weak gravitational lensing surveys. Most studies of intrinsic alignments have so far focused on…
We investigate the intrinsic alignments of dark halo substructures with their host halo major-axis orientations both analytically and numerically. Analytically, we derive the probability density distribution of the angles between the minor…
The subject of this paper is to build a physical model describing shape and size correlations of galaxies due to weak gravitational lensing and due to direct tidal interaction of elliptical galaxies with gravitational fields sourced by the…
Galactic rotation curves are crucial for understanding the distribution of mass in galaxies. Despite advances in precision observations, there are discrepancies between the inferred mass from luminosity and the observed rotational…
According to the well established hierarchical framework for galaxy evolution, galaxies grow through mergers with other galaxies and the LambdaCDM cosmological model predicts that the stellar halos of massive galaxies are rich in remnants…
Weak gravitational lensing is the slight distortion of galaxy shapes caused primarily by the gravitational effects of dark matter in the universe. In our work, we seek to invert the weak lensing signal from 2D telescope images to…
We perform an extensive analysis of nonlinear and stochastic biasing of galaxies and dark halos in spatially flat low-density CDM universe using cosmological hydrodynamic simulations. We compare their biasing properties with the predictions…
As a large-scale overdensity collapses, it affects the orientation and shape of galaxies that form, by exerting tidal shear along their axes. Therefore, the shapes of elliptical galaxies align with the tidal field of cosmic structures. This…
We present GLASS, the Generator for Large Scale Structure, a new code for the simulation of galaxy surveys for cosmology, which iteratively builds a light cone with matter, galaxies, and weak gravitational lensing signals as a sequence of…
The intrinsic alignment of galaxy shapes (by means of their angular momentum) and their cross-correlation with the surrounding dark matter tidal field are investigated using the 160 000, z=1.2 synthetic galaxies extracted from the…
Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics…
Galaxy morphology reflects structural properties which contribute to understand the formation and evolution of galaxies. Deep convolutional networks have proven to be very successful in learning hidden features that allow for unprecedented…
This work investigates the alignment of galactic spins with the cosmic web across cosmic time using the cosmological hydrodynamical simulation Horizon-AGN. The cosmic web structure is extracted via the persistent skeleton as implemented in…