Related papers: The CAMELS project: Cosmology and Astrophysics wit…
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…
We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous…
As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing…
We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and…
Galaxy formation models within cosmological hydrodynamical simulations contain numerous parameters with non-trivial influences over the resulting properties of simulated cosmic structures and galaxy populations. It is computationally…
The physics of baryons in halos, and their subsequent influence on the total matter phase space, has a rich phenomenology and must be well understood in order to pursue a vast set of questions in both cosmology and astrophysics. We use the…
The rapidly growing statistical precision of galaxy surveys has lead to a need for ever-more precise predictions of the observables used to constrain cosmological and galaxy formation models. The primary avenue through which such…
Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly understood way. This uncertainty is one of the main theoretical…
We quantify the cosmological spread of baryons relative to their initial neighboring dark matter distribution using thousands of state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS)…
We compare the structural properties and dark matter content of star-forming galaxies taken from the CAMELS cosmological simulations to the observed trends derived from the SPARC sample in the stellar mass range $[10^{9},…
We introduce the DREAMS project, an innovative approach to understanding the astrophysical implications of alternative dark matter models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise…
We present a study on the inference of cosmological and astrophysical parameters using stacked galaxy cluster profiles. Utilizing the CAMELS-zoomGZ simulations, we explore how various cluster properties--such as X-ray surface brightness,…
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 train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference. We employ Graph Neural Networks (GNNs), architectures designed to…
We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models are rotational, translational, and permutation invariant…
We introduce the Virgo Consortium's FLAMINGO suite of hydrodynamical simulations for cosmology and galaxy cluster physics. To ensure the simulations are sufficiently realistic for studies of large-scale structure, the subgrid prescriptions…
The evolution of the large-scale distribution of matter is sensitive to a variety of fundamental parameters that characterise the dark matter, dark energy, and other aspects of our cosmological framework. Since the majority of the mass…
In a novel approach employing implicit likelihood inference (ILI), also known as likelihood-free inference, we calibrate the parameters of cosmological hydrodynamic simulations against observations, which has previously been unfeasible due…
The CAMbridge Emission Line Surveyor (CAMELS) is a pathfinder program to demonstrate on-chip spectrometry at millimetre wavelengths. CAMELS will observe at frequencies from 103-114.7 GHz, providing 512 channels with a spectral resolution of…
Using a large sample of galaxies taken from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, a suite of hydrodynamic simulations varying both cosmological and astrophysical parameters, we train a…