On Going Research Topics
On Going Research Topics
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 simulations. As an example, we use the IllustrisTNG hydrodynamic simulation of a (75 Mpc/h)^3 volume to train our machine to predict e.g., stellar mass and star formation rate in a galaxy-sized halo based purely on its DM content. An extremely randomized tree (ERT) algorithm is used together with multiple novel improvements we introduce here such as a refined error function in machine training and two-stage learning. Aided by these improvements, our model demonstrates a significantly increased accuracy in predicting baryonic properties compared to prior attempts — in other words, the machine better mimics IllustrisTNG’s galaxy-halo correlation. By applying our machine to the MultiDark-Planck DM-only simulation of a large (1 Gpc/h)^3 volume, we then validate the pipeline that rapidly generates a galaxy catalogue from a DM halo catalogue using the correlations the machine found in IllustrisTNG. We also compare our galaxy catalogue with the ones produced by popular semi-analytic models (SAMs). Our so-called machine-assisted semi-simulation model (MSSM) is shown to be largely compatible with SAMs, and may become a promising method to transplant the baryon physics of galaxy-scale hydrodynamic calculations onto a larger-volume DM-only run. We discuss the benefits that machine-based approaches like this entail, as well as suggestions to raise the scientific potential of such approaches.
● Jo, Y., & Kim, J. -H., “Machine-assisted Semi-Simulation Model (MSSM): Estimating Galactic Baryonic Properties from Their Dark Matter Using A Machine Trained on Hydrodynamic Simulations” , MNRAS 489 (2019) 3565
As computational resolution of modern cosmological simulations come closer to resolving individual star-forming clumps in a galaxy, the need for“resolution-appropriate”physics for a galaxy-scale simulation has never been greater. To this end, we introduce a self-consistent numerical framework that includes explicit treatments of feedback from star-forming molecular clouds (SFMCs) and massive black holes (MBHs). In addition to the thermal supernovae feedback from SFMC particles, photo-ionizing radiation from both SFMCs and MBHs is tracked through full three-dimensional ray tracing. A mechanical feedback channel from MBHs is also considered.Using our framework, we perform a state-of-the-art cosmological simulation of a quasar-host galaxy at z∼7.5 for∼25 Myr with all relevant galactic components, such as dark matter, gas, SFMCs, and an embedded MBH seed of 10^6M_sun. We find that feedback from SFMCs and an accreting MBH suppresses runaway star formation locally in the galactic core region. Newly included radiation feedback from SFMCs, combined with feedback from the MBH,helps the MBH grow faster by retaining gas that eventually accretes on to the MBH. Our experiment demonstrates that previously undiscussed types of interplay between gas, SFMCs, and a MBH may hold important clues about the growth and feedback of quasars and their host galaxies in the high-redshift universe.
● Kim, J. -H., Wise, J. H., Abel, T., Jo, Y., Primack, J. R., & Hopkins, P. F., “High-redshift Galaxy Formation with Self-consistently Modeled Stars and Massive Black Holes: Stellar Feedback and Quasar Growth” , ApJ 887 (2019) 120
● Jo, Y., & Kim, J. -H., “Impact of Gas Inflow on Quasar Growth at High-redshift using Passive Scalar (subject to change)” , 2020 in progress