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Found 4 papers, 1 papers with code
Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes
In doing so, we are able to recover more properties than the analytic formalism alone can provide, creating a high-speed hydrodynamic simulation emulator that populates galactic dark matter haloes in N-body simulations with baryonic properties.
Mergers, Starbursts, and Quenching in the Simba Simulation
We use the Simba cosmological galaxy formation simulation to investigate the relationship between major mergers ($\leq$ 4:1), starbursts, and galaxy quenching.
Astrophysics of Galaxies
Predicting the Neutral Hydrogen Content of Galaxies From Optical Data Using Machine Learning
When we train on mock data from Mufasa and test on RESOLVE, this increases to {\sc rmse}$\sim0. 45$.
Astrophysics of Galaxies
Painting galaxies into dark matter halos using machine learning
Our ML framework takes input halo properties including halo mass, environment, spin, and recent growth history, and outputs central galaxy and halo baryonic properties including stellar mass ($M_*$), star formation rate (SFR), metallicity ($Z$), neutral ($\rm HI$) and molecular ($\rm H_2$) hydrogen mass.
Astrophysics of Galaxies
