ASURA-FDPS-ML: Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback

We introduce new high-resolution galaxy simulations accelerated by a surrogate model that reduces the computation cost by approximately 75 percent. Massive stars with a Zero Age Main Sequence mass of about 8 solar masses and above explode as core-collapse supernovae (CCSNe), which play a critical role in galaxy formation. The energy released by CCSNe is essential for regulating star formation and driving feedback processes in the interstellar medium (ISM). However, the short integration timesteps required for SNe feedback present significant bottlenecks in star-by-star galaxy simulations that aim to capture individual stellar dynamics and the inhomogeneous shell expansion of SNe within the turbulent ISM. Our new framework combines direct numerical simulations and surrogate modeling, including machine learning and Gibbs sampling. The star formation history and the time evolution of outflow rates in the galaxy match those obtained from resolved direct numerical simulations. Our new approach achieves high-resolution fidelity while reducing computational costs, effectively bridging the physical scale gap and enabling multi-scale simulations.

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. Our models are rotationally, translationally, and permutation invariant and have no scale cutoff. By training on galaxy catalogs that only contain the 3D positions and radial velocities of approximately $1,000$ galaxies in tiny volumes of $(25~h^{-1}{\rm Mpc})^3$, our models achieve a precision of approximately $12$% when inferring the value of $\Omega_{\rm m}$. To test the robustness of our models, we evaluated their performance on galaxy catalogs from thousands of hydrodynamic simulations, each with different efficiencies of supernova and AGN feedback, run with five different codes and subgrid models, including IllustrisTNG, SIMBA, Astrid, Magneticum, and SWIFT-EAGLE. Our results demonstrate that our models are robust to astrophysics, subgrid physics, and subhalo/galaxy finder changes. Furthermore, we test our models on 1,024 simulations that cover a vast region in parameter space - variations in 5 cosmological and 23 astrophysical parameters - finding that the model extrapolates really well. Including both positions and velocities are key to building robust models, and our results indicate that our networks have likely learned an underlying physical relation that does not depend on galaxy formation and is valid on scales larger than, at least, $~\sim10~h^{-1}{\rm kpc}$.

Robust field-level inference with dark matter halos

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 $\gtrsim 10^{10}~h^{-1}M_\odot$ in a periodic volume of $(25~h^{-1}{\rm Mpc})^3$; every halo in the catalogue is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of $\Omega_{\rm m}$ and $\sigma_8$ with a mean relative error of $\sim6\%$, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of $\Omega_{\rm m}$ and $\sigma_8$ when tested using halo catalogues from thousands of N-body simulations run with five different N-body codes: Abacus, CUBEP$^3$M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer $\Omega_{\rm m}$ also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N-body codes are not converged on the relevant scales corresponding to these parameters.

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. CAMELS contains 4,233 cosmological simulations, 2,049 N-body and 2,184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogues, power spectra, bispectra, Lyman-$\alpha$ spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over one thousand catalogues that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz Semi-Analytic Model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies and summary statistics. We provide further technical details on how to access, download, read, and process the data at \url{https://camels.readthedocs.io}.