69 citations · 297 across the 14 of their papers we have counts for
15 papers
Constructing a Mock Galaxy Catalog for the All-sky SPECtroscopic Survey of Nearby Galaxies (A-SPEC) Using the Machine-assisted Semi-Simulation Model
Dongkok Kim, Yongseok Jo, Ho Seong Hwang +26
We present a methodology for constructing a mock galaxy catalog for the All-sky SPECtroscopic survey of nearby galaxies (A-SPEC) using the Machine-assisted Semi-Simulation Model. T…
Learning the Universe with the 2nd Generation of CAMELS: Varying 35 parameters of the IllustrisTNG model in (50Mpc/h)^3 boxes
Shy Genel, Yongseok Jo, Boon Kiat Oh +10
We present a new set of 1,192 cosmological simulations as part of the CAMELS project, in which a space of 35 cosmological, astrophysical, and numerical parameters is explored aroun…
From Dense Gas Clouds to Supermassive Black Hole Seeds: Hybrid Hydro/Direct -body Simulations of Runaway Collision-driven Intermediate-mass Black Hole Formation
Eunwoo Chung, Yongseok Jo, Ji-hoon Kim +2
A population of dense stellar systems at high redshift has recently been uncovered by the JWST. To investigate the formation of supermassive black hole (SMBH) seeds in these dense…
Evolution of Nuclear Star Cluster in Dwarf Galaxy through Mergers and In-Situ Star Formation
Yongseok Jo, Minyong Jung, Greg L. Bryan +3
Nuclear Star Clusters (NSCs) are dense stellar systems located at the centers of galaxies. Employing Enzo-Abyss, which integrates hydrodynamics with a direct N-body solver, we intr…
Towards Robustness Across Cosmological Simulation Models TNG, SIMBA, ASTRID, and EAGLE
Yongseok Jo, Shy Genel, Anirvan Sengupta +3
The rapid advancement of large-scale cosmological simulations has opened new avenues for cosmological and astrophysical research. However, the increasing diversity among cosmologic…
On the Significance of Covariance for Constraining Theoretical Models From Galaxy Observables
Yongseok Jo, Shy Genel, Joel Leja +1
In this study, we investigate the impact of covariance within uncertainties on the inference of cosmological and astrophysical parameters, specifically focusing on galaxy stellar m…
Inferring Cosmological Parameters on SDSS via Domain-Generalized Neural Networks and Lightcone Simulations
Jun-Young Lee, Ji-hoon Kim, Minyong Jung +6
We present a proof-of-concept simulation-based inference on and from the SDSS BOSS LOWZ NGC catalog using neural networks and domain generalization techniques w…
Evolution of Star Cluster Within Galaxy using Self-consistent Hybrid Hydro/N-body Simulation
Yongseok Jo, Seoyoung Kim, Ji-hoon Kim +1
We introduce a GPU-accelerated hybrid hydro/N-body code (Enzo-N) designed to address the challenges of concurrently simulating star clusters and their parent galaxies. This task ha…
The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites
Yueying Ni, Shy Genel, Daniel Anglés-Alcázar +12
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 tha…
Calibrating cosmological simulations with implicit likelihood inference using galaxy growth observables
Yongseok Jo, Shy Genel, Benjamin Wandelt +7
In a novel approach employing implicit likelihood inference (ILI), also known as likelihood-free inference, we calibrate the parameters of cosmological hydrodynamic simulations aga…
The CAMELS project: public data release
Francisco Villaescusa-Navarro, Shy Genel, Daniel Anglés-Alcázar +44
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynami…
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar +25
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 containi…
Dark Matter Deficient Galaxies Produced Via High-velocity Galaxy Collisions In High-resolution Numerical Simulations
Eun-jin Shin, Minyong Jung, Goojin Kwon +4
The recent discovery of diffuse dwarf galaxies that are deficient in dark matter appears to challenge the current paradigm of structure formation in our Universe. We describe the n…
High-redshift Galaxy Formation with Self-consistently Modeled Stars and Massive Black Holes: Stellar Feedback and Quasar Growth
Ji-hoon Kim, John H. Wise, Tom Abel +3
As computational resolution of modern cosmological simulations reach ever so close to resolving individual star-forming clumps in a galaxy, a need for "resolution-appropriate" phys…
Machine-assisted Semi-Simulation Model (MSSM): Estimating Galactic Baryonic Properties from their Dark Matter using a Machine Trained on Hydrodynamic Simulations
Yongseok Jo, Ji-hoon Kim
We present a pipeline to estimate baryonic properties of a galaxy inside a dark matter (DM) halo in DM-only simulations using a machine trained on high-resolution hydrodynamic simu…