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Found 25 papers, 7 papers with code
The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites
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 frameworks of CAMELS-TNG and CAMELS-SIMBA, to provide broader training sets and testing grounds for machine-learning algorithms designed for cosmological studies.
Robust Field-level Likelihood-free Inference with Galaxies
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.
The CAMELS project: public data release
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.
Weighing the Milky Way and Andromeda with Artificial Intelligence
We present new constraints on the masses of the halos hosting the Milky Way and Andromeda galaxies derived using graph neural networks.
Inferring halo masses with Graph Neural Networks
Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method.
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The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
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 stars from 2, 000 distinct simulated universes at several cosmic times.
Robust marginalization of baryonic effects for cosmological inference at the field level
We train neural networks to perform likelihood-free inference from $(25\, h^{-1}{\rm Mpc})^2$ 2D maps containing the total mass surface density from thousands of hydrodynamic simulations of the CAMELS project.
Multifield Cosmology with Artificial Intelligence
Although our maps only cover a small area of $(25~h^{-1}{\rm Mpc})^2$, and the different fields are contaminated by astrophysical effects in very different ways, our networks can infer the values of $\Omega_{\rm m}$ and $\sigma_8$ with a few percent level precision for most of the fields.
Neural networks as optimal estimators to marginalize over baryonic effects
For this data, we show that neural networks can 1) extract the maximum available cosmological information, 2) marginalize over baryonic effects, and 3) extract cosmological information that is buried in the regime dominated by baryonic physics.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
From Dark Matter to Galaxies with Convolutional Neural Networks
Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations.
The Role of Machine Learning in the Next Decade of Cosmology
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
The IllustrisTNG Simulations: Public Data Release
IllustrisTNG is a suite of large volume, cosmological, gravo-magnetohydrodynamical simulations run with the moving-mesh code Arepo.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Statistical properties of paired fixed fields
We quantify the statistical improvement brought by these simulations, over standard ones, on different power spectra such as matter, halos, CDM, gas, stars, black-holes and magnetic fields, finding that they can reduce their variance by factors as large as $10^6$.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Supermassive black holes and their feedback effects in the IllustrisTNG simulation
We show that the quenching of massive central galaxies happens coincidently with kinetic-mode feedback, consistent with the notion that active supermassive black cause the low specific star formation rates observed in massive galaxies.
Astrophysics of Galaxies High Energy Astrophysical Phenomena
First results from the IllustrisTNG simulations: radio haloes and magnetic fields
Using two simple models for the energy distribution of relativistic electrons we predict the diffuse radio emission of $280$ clusters with a baryonic mass resolution of $1. 1\times 10^{7}\,{\rm M_{\odot}}$, and generate mock observations for VLA, LOFAR, ASKAP and SKA.
Cosmology and Nongalactic Astrophysics
First results from the IllustrisTNG simulations: matter and galaxy clustering
The two-point correlation function of the simulated galaxies agrees well with SDSS at its mean redshift z ~ 0. 1, both as a function of stellar mass and when split according to galaxy colour, apart from a mild excess in the clustering of red galaxies in the stellar mass range 10^9-10^10 Msun/h^2.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
First results from the IllustrisTNG simulations: the stellar mass content of groups and clusters of galaxies
Total halo mass is a very good predictor of stellar mass, and vice versa: at $z=0$, the 3D stellar mass measured within 30 kpc scales as $\propto (M_{\rm 500c})^{0. 49}$ with a $\sim 0. 12$ dex scatter.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
First results from the IllustrisTNG simulations: the galaxy color bimodality
We find a striking improvement with respect to the original Illustris simulation, as well as excellent quantitative agreement in comparison to the observations, with a sharp transition in median color from blue to red at a characteristic M* ~ 10^10. 5 Msun.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
First results from the IllustrisTNG simulations: A tale of two elements -- chemical evolution of magnesium and europium
To this end we use the magnesium to iron ratio as a proxy for the effects of our SNII and SNIa metal return prescription, and a means to compare our simulated abundances to a wide variety of galactic observations.
Astrophysics of Galaxies
Simulating Galaxy Formation with the IllustrisTNG Model
The overall framework builds upon the successes of the Illustris galaxy formation model, and includes prescriptions for star formation, stellar evolution, chemical enrichment, primordial and metal-line cooling of the gas, stellar feedback with galactic outflows, and black hole formation, growth and multi-mode feedback.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
The Illustris simulation: the evolving population of black holes across cosmic time
We find that the black hole mass density for redshifts z = 0 - 5 and the black hole mass function at z = 0 predicted by Illustris are in very good agreement with the most recent observational constraints.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
Introducing the Illustris Project: the evolution of galaxy populations across cosmic time
In particular, we discuss (a) the buildup of galactic mass, showing stellar mass functions and the relations between stellar mass and halo mass from z=7 to z=0, (b) galaxy number density profiles around massive central galaxies out to z=4, (c) the gas and total baryon content of both galaxies and their halos for different redshifts, and as a function of mass and radius, and (d) the evolution of galaxy specific star-formation rates up to z=8.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
Properties of galaxies reproduced by a hydrodynamic simulation
Previous simulations of the growth of cosmic structures have broadly reproduced the 'cosmic web' of galaxies that we see in the Universe, but failed to create a mixed population of elliptical and spiral galaxies due to numerical inaccuracies and incomplete physical models.
Cosmology and Nongalactic Astrophysics
A model for cosmological simulations of galaxy formation physics
This is required to simultaneously reproduce the stellar mass content of low mass haloes and their gas oxygen abundances.
Cosmology and Nongalactic Astrophysics
