Sagan Sutherland | Searcharxiv

arXiv2026

Cosmological back-reaction of baryons on dark matter in the CAMELS simulations

Baryonic processes such as radiative cooling and feedback from massive stars and active galactic nuclei (AGN) directly redistribute baryons in the Universe but also indirectly redistribute dark matter due to changes in the gravitational potential. In this work, we investigate this "back-reaction" of baryons on dark matter using thousands of cosmological hydrodynamic simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, including parameter variations in the SIMBA, IllustrisTNG, ASTRID, and Swift-EAGLE galaxy formation models. Matching haloes to corresponding N-body (dark matter-only) simulations, we find that virial masses decrease owing to the ejection of baryons by feedback. Relative to N-body simulations, halo profiles show an increased dark matter density in the center (due to radiative cooling) and a decrease in density farther out (due to feedback), with both effects being strongest in SIMBA (> 450% increase at r < 0.01 Rvir). The clustering of dark matter strongly responds to changes in baryonic physics, with dark matter power spectra in some simulations from each model showing as much as 20% suppression or increase in power at k ~ 10 h/Mpc relative to N-body simulations. We find that the dark matter back-reaction depends intrinsically on cosmology (Omega_m and sigma_8) at fixed baryonic physics, and varies strongly with the details of the feedback implementation. These results emphasize the need for marginalizing over uncertainties in baryonic physics to extract cosmological information from weak lensing surveys as well as their potential to constrain feedback models in galaxy evolution.

astro-ph.CO↗

arXiv2023

$\texttt{slick}$: Modeling a Universe of Molecular Line Luminosities in Hydrodynamical Simulations

We present {\sc slick} (the Scalable Line Intensity Computation Kit), a software package that calculates realistic CO, [\ion{C}{1}], and [\ion{C}{2}] luminosities for clouds and galaxies formed in hydrodynamic simulations. Built on the radiative transfer code {\sc despotic}, {\sc slick} computes the thermal, radiative, and statistical equilibrium in concentric zones of model clouds, based on their physical properties and individual environments. We validate our results applying {\sc slick} to the high-resolution run of the {\sc Simba} simulations, testing the derived luminosities against empirical and theoretical/analytic relations. To simulate the line emission from a universe of emitting clouds, we have incorporated random forest machine learning (ML) methods into our approach, allowing us to predict cosmologically evolving properties of CO, [\ion{C}{1}] and [\ion{C}{2}] emission from galaxies such as luminosity functions. We tested this model in 100,000 gas particles, and 2,500 galaxies, reaching an average accuracy of $\sim$99.8\% for all lines. Finally, we present the first model light cones created with realistic and ML-predicted CO, [\ion{C}{1}], and [\ion{C}{2}] luminosities in cosmological hydrodynamical simulations, from $z=0$ to $z=10$.

astro-ph.GA↗