Related papers: Generating Synthetic Cosmological Data with GalSam…
We present cogsworth, an open-source Python tool for producing self-consistent population synthesis and galactic dynamics simulations. With cogsworth one can (1) sample a population of binaries and star formation history, (2) perform rapid…
The Cosmology Data Management system (CosmoDM) is an automated and flexible data management system for the processing and calibration of data from optical photometric surveys. It is designed to run on supercomputers and to minimize disk I/O…
Upcoming large astronomical surveys are expected to capture an unprecedented number of strong gravitational lensing systems. Deep learning is emerging as a promising practical tool for the detection and quantification of these galaxy-scale…
We describe a new, free and open source semi-analytic model of galaxy formation, Galacticus. The Galacticus model was designed to be highly modular to facilitate expansion and the exploration of alternative descriptions of key physical…
Strong lensing has developed into an important astrophysical tool for probing both cosmology and galaxies (their structures, formations, and evolutions). Now several hundreds of strong lens systems produced by massive galaxies have been…
We perform the first direct cosmological and astrophysical parameter inference from the combination of galaxy luminosity functions and colours using a simulation based inference approach. Using the Synthesizer code we simulate the dust…
We present a method for populating dark matter simulations with haloes of mass below the resolution limit. It is based on stochastically sampling a field derived from the density field of the halo catalogue, using constraints from the…
Mock halo catalogues are indispensable data products for developing and validating cosmological inference pipelines. A major challenge in generating mock catalogues is modelling the halo or galaxy bias, which is the mapping from matter…
We present new numerical tools to analyse cosmic void catalogues, implemented inside the CosmoBolognaLib, a large set of Open Source C++/Python numerical libraries. The CosmoBolognaLib provides a common numerical environment for…
We present a suite of 18 synthetic sky catalogs designed to support science analysis of galaxies in the Dark Energy Survey Year 1 (DES Y1) data. For each catalog, we use a computationally efficient empirical approach, ADDGALS, to embed…
We present 2000 mock galaxy catalogs for the analysis of baryon acoustic oscillations in the Emission Line Galaxy (ELG) sample of the Extended Baryon Oscillation Spectroscopic Survey Data Release 16 (eBOSS DR16). Each mock catalog has a…
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…
To understand the universe and to interpret the cosmological parameters governing its evolution it is necessary to contrast the data from galaxy surveys with simulation. Typically it entails using computationally expensive N -body…
We show that a Denoising Diffusion Probabalistic Model (DDPM), a class of score-based generative model, can be used to produce realistic mock images that mimic observations of galaxies. Our method is tested with Dark Energy Spectroscopic…
The number density of galaxy clusters across mass and redshift has been established as a powerful cosmological probe. Cosmological analyses with galaxy clusters traditionally employ scaling relations. However, many challenges arise from…
Strongly gravitational lensing systems (SGL) encodes cosmology information in source/lens distance ratios $\mathcal{D}_{\rm obs}=\mathcal{D}_{\rm ls}/\mathcal{D}_{\rm s}$, which can be used to precisely constrain cosmological parameters. In…
Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches $\sim20\Gpchcube$. It is a great challenge to prepare high-resolution simulations with…
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…
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4,233…
Gravitational lensing offers unique insights into cosmology by bending light around massive objects. Strong gravitational lensing, in particular, produces magnified and often multiple images of distant sources, crucial for precise…