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In the era of precision cosmology, the ability to generate accurate and large-scale galaxy catalogs is crucial for advancing our understanding of the universe. With the flood of cosmological data from current and upcoming missions,…
The shapes of galaxies are not randomly oriented on the sky. During the galaxy formation and evolution process, environment has a strong influence, as tidal gravitational fields in the large-scale structure tend to align nearby galaxies.…
Machine Learning on graph-structured data is an important and omnipresent task for a vast variety of applications including anomaly detection and dynamic network analysis. In this paper, a deep generative model is introduced to capture…
The connections among galaxies, the dark matter halos where they form and the properties of the large-scale Cosmic Web still need to be completely disentangled. We use the cosmological hydrodynamical simulation TNG100 of the IllustrisTNG…
Modelling star-forming galaxies is crucial for upcoming observations of large-scale matter and galaxy distributions with galaxy redshift surveys and line intensity mapping (LIM). We introduce CosmoGLINT (Cosmological Generative model for…
Context. Generative models open up the possibility to interrogate scientific data in a more data-driven way. Aims: We propose a method that uses generative models to explore hypotheses in astrophysics and other areas. We use a neural…
Semi-analytic models are a widely used approach to simulate galaxy properties within a cosmological framework, relying on simplified yet physically motivated prescriptions. They have also proven to be an efficient alternative for generating…
Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This…
The classification of galaxies as spirals or ellipticals is a crucial task in understanding their formation and evolution. With the arrival of large-scale astronomical surveys, such as the Sloan Digital Sky Survey (SDSS), astronomers now…
Multi-band images of galaxies reveal a huge amount of information about their morphology and structure. However, inferring properties of the underlying stellar populations such as age, metallicity or kinematics from those images is…
Applying a new estimator to the Dark Energy Survey (DES) Y3 weak lensing shape catalog, we map the galaxies' orientation field and report the first detection of a large-scale axial intrinsic alignment (LAIA) on dipolar angular scales.…
The intrinsic correlations of galaxy shapes and orientations across the large-scale structure of the Universe are a known contaminant to weak gravitational lensing. They are known to be dependent on galaxy properties, such as their mass and…
We show that the spatial correlation of the intrinsic alignments (IAs) of galaxies, measured in galaxy redshift surveys, offers a precision route to improve the geometrical and dynamical constraints on cosmology. The IA has been treated as…
Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials. As an exhaustive exploration of the vast chemical space is still infeasible, we…
The possibility to constrain cosmological parameters from galaxy surveys using field-level machine learning methods that bypass traditional summary statistics analyses, depends crucially on our ability to generate simulated training sets.…
We show with analytic models that the assumption of uncorrelated intrinsic ellipticities of target sources that is usually made in searches for weak gravitational lensing due to large-scale mass inhomogeneities (``field lensing'') is…
Upcoming cosmological surveys will provide unprecedented amount of data, which will require innovative statistical methods to maximize the scientific exploitation. Standard cosmological analyses based on abundances, two-point and…
The intrinsic alignment of galaxies is a key factor in modeling weak-lensing observations and can serve as a valuable signal for both cosmological and astrophysical studies. Modelling this signal requires understanding how galaxy shapes…
Near-future large galaxy surveys will encounter blended galaxy images at a fraction of up to 50% in the densest regions of the universe. Current deblending techniques may segment the foreground galaxy while leaving missing pixel intensities…
Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological…