1.1k citations · 1.1k across the 2 of their papers we have counts for
6 papers
Classifying galaxies according to their HI content
Sambatra Andrianomena, Mika Rafieferantsoa, Romeel Davé
We use machine learning to classify galaxies according to their HI content, based on both their optical photometry and environmental properties. The data used for our analyses are…
Simba: Cosmological Simulations with Black Hole Growth and Feedback
Romeel Davé, Daniel Anglés-Alcázar, Desika Narayanan +3
We introduce the Simba simulations, the next generation of the Mufasa cosmological galaxy formation simulations run with Gizmo's meshless finite mass hydrodynamics. Simba includes…
Timescales for Hi consumption and SFR depletion of satellite galaxies in groups
Mika Rafieferantsoa, Romeel Davé, Thorsten Naab
We investigate the connection between the HI~content, SFR and environment of galaxies using a hydrodynamic simulation that incorporates scaling relations for galactic wind and a he…
IQ-Collaboratory 1.1: the Star-Forming Sequence of Simulated Central Galaxies
ChangHoon Hahn, Tjitske K. Starkenburg, Ena Choi +12
A tightly correlated star formation rate-stellar mass relation of star forming galaxies, or star-forming sequence (SFS), is a key feature in galaxy property-space that is predicted…
Predicting the Neutral Hydrogen Content of Galaxies From Optical Data Using Machine Learning
Mika Rafieferantsoa, Sambatra Andrianomena, Romeel Davé
We develop a machine learning-based framework to predict the HI content of galaxies using more straightforwardly observable quantities such as optical photometry and environmental…
Mufasa:The strength and evolution of galaxy conformity in various tracers
Mika Rafieferantsoa, Romeel Davé
We investigate galaxy conformity using the Mufasa cosmological hydrodynamical simulation. We show a bimodal distribution in galaxy colour with radius, albeit with too many low-mass…