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Galaxy groups are more than an intermediate scale between clusters and halos hosting individual galaxies, they are crucial laboratories capable of testing a range of astrophysics from how galaxies form and evolve to large scale structure…
The morphology of a galaxy has been shown to encode the evolutionary history and correlates strongly with physical properties such as stellar mass, star formation rates and past merger events. While the majority of galaxies in the local…
Large volume cosmological simulations succeed in reproducing the large-scale structure of the Universe. However, they lack resolution and may not take into account all relevant physical processes to test if the detail properties of galaxies…
We forecast the future constraints on scale-dependent parametrizations of galaxy bias and their impact on the estimate of cosmological parameters from the power spectrum of galaxies measured in a spectroscopic redshift survey. For the…
The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe follows an intricate pattern now famously known as the Large-Scale Structure or the Cosmic Web. To study the environments of this network,…
Context. As recently demonstrated, high-z submillimetre galaxies (SMGs) are the perfect background sample for tracing the mass density profiles of galaxies and clusters (baryonic and dark matter) and their time-evolution through…
In order to shed light on how galactic properties depend on the intergalactic medium (IGM) environment traced by the Ly$\alpha$ forest, we observationally investigate the IGM-galaxy connection using the publicly available 3D IGM tomography…
We demonstrate the use of deep network to learn the distribution of data from state-of-the-art hydrodynamic simulations of the CAMELS project. To this end, we train a generative adversarial network to generate images composed of three…
Based on the cosmological principle only, the method of describing the evolution of the Universe, called cosmography, is in fact a kinematics of cosmological expansion. The effectiveness of cosmography lies in the fact that it allows, based…
The abundance of galaxy clusters is in principle a powerful tool to constrain cosmological parameters, especially $\Omega_\mathrm{m}$ and $\sigma_8$, due to the exponential dependence in the high-mass regime. While the best observables are…
Cosmological probes pose an inverse problem where the measurement result is obtained through observations, and the objective is to infer values of model parameters which characterize the underlying physical system -- our Universe. Modern…
Cosmic voids are underdense regions within the large-scale structure of the Universe, spanning a wide range of physical scales - from a few megaparsecs (Mpc) to the largest observable structures. Their distinctive properties make them…
In the context of cosmography approach and using the data of Hubble diagram for supernovae, quasars and gamma-ray bursts, we study some DE parametrizations and also the concordance $\Lambda$CDM universe. Using the different combinations of…
We investigate the potentiality of using strong lensing clusters to constrain the cosmological parameters Omega_m and Omega_lambda. The existence of a multiple image system with known redshift allows, for a given (Omega_m, Omega_lambda)…
We introduce a novel technique for constraining cosmological parameters and galaxy assembly bias using non-linear redshift-space clustering of galaxies. We scale cosmological N-body simulations and insert galaxies with the SubHalo Abundance…
We present the first cosmological constraints from a joint analysis of galaxy clustering and galaxy-galaxy lensing using extended SubHalo Abundance Matching (SHAMe). We analyse stellar mass-selected Galaxy And Mass Assembly (GAMA) galaxy…
Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the…
We present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy…
We study the impact of warm dark matter mass on the internal properties of individual galaxies using a large suite of 1,024 state-of-the-art cosmological hydrodynamic simulations from the DREAMS project. We take individual galaxies'…
We train graph neural networks on halo catalogues from Gadget N-body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogues contain $\lesssim$5,000 halos with masses $\gtrsim…