ALBERT

Description: We extend a machine learning (ML) framework presented previously to model galaxy formation and evolution in a hierarchical universe using N -body + hydrodynamical simulations. In this work, we show that ML is a promising technique to study galaxy formation in the backdrop of a hydrodynamical simulation. We use the Illustris simulation to train and test various sophisticated ML algorithms. By using only essential dark matter halo physical properties and no merger history, our model predicts the gas mass, stellar mass, black hole mass, star formation rate, g – r colour, and stellar metallicity fairly robustly. Our results provide a unique and powerful phenomenological framework to explore the galaxy–halo connection that is built upon a solid hydrodynamical simulation. The promising reproduction of the listed galaxy properties demonstrably place ML as a promising and a significantly more computationally efficient tool to study small-scale structure formation. We find that ML mimics a full-blown hydrodynamical simulation surprisingly well in a computation time of mere minutes. The population of galaxies simulated by ML, while not numerically identical to Illustris, is statistically robust and physically consistent with Illustris galaxies and follows the same fundamental observational constraints. ML offers an intriguing and promising technique to create quick mock galaxy catalogues in the future.

Description: Cosmological hydrodynamical simulations of galaxy evolution are increasingly able to produce realistic galaxies, but the largest hurdle remaining is in constructing subgrid models that accurately describe the behaviour of stellar feedback. As an alternate way to test and calibrate such models, we propose to focus on the circumgalactic medium (CGM). To do so, we generate a suite of adaptive mesh refinement simulations for a Milky-Way-massed galaxy run to z  = 0, systematically varying the feedback implementation. We then post-process the simulation data to compute the absorbing column density for a wide range of common atomic absorbers throughout the galactic halo, including H  i , Mg  ii , Si  ii , Si  iii , Si  iv , C  iv , N  v , O  vi and O  vii . The radial profiles of these atomic column densities are compared against several quasar absorption line studies to determine if one feedback prescription is favoured. We find that although our models match some of the observations (specifically those ions with lower ionization strengths), it is particularly difficult to match O  vi observations. There is some indication that the models with increased feedback intensity are better matches. We demonstrate that sufficient metals exist in these haloes to reproduce the observed column density distribution in principle, but the simulated CGM lacks significant multiphase substructure and is generally too hot. Furthermore, we demonstrate the failings of inflow-only models (without energetic feedback) at populating the CGM with adequate metals to match observations even in the presence of multiphase structure. Additionally, we briefly investigate the evolution of the CGM from z  = 3 to present. Overall, we find that quasar absorption line observations of the gas around galaxies provide a new and important constraint on feedback models.

Description: Starlight from galaxies plays a pivotal role throughout the process of cosmic reionization. We present the statistics of dwarf galaxy properties at z  〉 7 in haloes with masses up to 10 9 M , using a cosmological radiation hydrodynamics simulation that follows their buildup starting with their Population III progenitors. We find that metal-enriched star formation is not restricted to atomic cooling ( T vir ≥ 10 4 K) haloes, but can occur in haloes down to masses ~10 6 M , especially in neutral regions. Even though these smallest galaxies only host up to 10 4 M of stars, they provide nearly 30 per cent of the ionizing photon budget. We find that the galaxy luminosity function flattens above M UV  ~ –12 with a number density that is unchanged at z 10. The fraction of ionizing radiation escaping into the intergalactic medium is inversely dependent on halo mass, decreasing from 50 to 5 per cent in the mass range log M /M = 7.0-8.5. Using our galaxy statistics in a semi-analytic reionization model, we find a Thomson scattering optical depth consistent with the latest Planck results, while still being consistent with the UV emissivity constraints provided by Lyα forest observations at z  = 4–6.

Description: We present a new exploratory framework to model galaxy formation and evolution in a hierarchical Universe by using machine learning (ML). Our motivations are two-fold: (1) presenting a new, promising technique to study galaxy formation, and (2) quantitatively analysing the extent of the influence of dark matter halo properties on galaxies in the backdrop of semi-analytical models (SAMs). We use the influential Millennium Simulation and the corresponding Munich SAM to train and test various sophisticated ML algorithms (k-Nearest Neighbors, decision trees, random forests, and extremely randomized trees). By using only essential dark matter halo physical properties for haloes of M 〉 10 12 M and a partial merger tree, our model predicts the hot gas mass, cold gas mass, bulge mass, total stellar mass, black hole mass and cooling radius at z = 0 for each central galaxy in a dark matter halo for the Millennium run. Our results provide a unique and powerful phenomenological framework to explore the galaxy–halo connection that is built upon SAMs and demonstrably place ML as a promising and a computationally efficient tool to study small-scale structure formation.