Neutral hydrogen in galaxies, its content and the effect of environment on its evolution
Rafieferantsoa, Mika Harisetry
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Using two hydrodynamic galaxy formation simulations from the Mufasa project that I helped develop, we aim to better understand the relationship between galaxy evolution and its cold gas content commonly known as the neutral hydrogen or Hi. We first look at the environmental properties of the simulated galaxies and compare to those that are available observationally. As a proxy, we specifically quantify the so-called galactic conf ormity, which is the concordance between the properties of galaxies neighbouring the primaries, in chapter 2. We show that the Hi, the specific star formation rate (sSFR) and the colour of galaxies show galactic conformity in qualitative agreement with previous observed data, i.e. the Hi-rich primary galaxies are surrounded by Hi-richer galaxies than the Hi-poor primary galaxies, and similarly for the sSFR and the colour. We find that environment, quantified by the number of neigbouring galaxies within a fixed aperture, stellar age and molecular hydrogen (H2) also show conformity. Galactic conformity also depends on the dark matter halo mass of the primary galaxy. The galactic conformity signal from the primaries of smaller haloes is weak but extends out to several virial radii of those structures, whereas the signal is very strong for high mass haloes but lowers quickly with distances from the primaries. We also find the galactic conformity only emerges in the later half of cosmic evolution. We next quantify the gas content and star formation depletion timescales in chapter 3. We use two carefully chosen groups of simulated galaxies and find that timescales are affected by both the mass of the virialised structure of the first infall and the galaxy stellar mass at infall: the higher the halo mass or the stellar mass the shorter the timescale. The gas or Hi depletion timescale is concordant to that of the star formation quenching, indicative of direct decrease of SFR due to depletion of the extended cold gas reservoir. The neutral atomic or molecular hydrogen consumption timescale depends on the Hubble time. Galaxies tend to form stars more efficiently at lower redshift. While the halo mass of infall affects the consumption timescale of the Hi, it does not correlate with the H2. We lastly develop machine learning tools to use galaxy photometric data to predict a galaxy’s Hi mass in chapter 4, to allow predictions for Hi from much larger optical photometric surveys. The training and testing of the algorithms are done first with the simulated data from Mufasa. We show that our model performs better than previously done with ad hoc data fitting approaches. Random Forest (RF) followed by the Deep Neural Networks (DNN) perform best among the explored machine learning techniques. Extending the trained models to observed data, namely the Arecibo Legacy Fast ALFA (ALFALFA) and REsolved Spectroscopy Of a Local VolumE (RESOLVE) survey data, we show the overall performance is slightly reduced relative to the simulated testing set owing to the small inconsistency between definition of galaxy properties between simulation and observational data, and DNN perfoms the best in this case. The application of our methods is useful for galaxy-by-galaxy predictions and anticipated to correct for incompletness in the upcoming Hi deep surveys done with MeerKAT and eventually the Square Kilometre Array (SKA).