NIHAO XIX: How supernova feedback shapes the galaxy baryon cycle

We have used the NIHAO simulations to explore how supernovae (SNe) affect star formation in galaxies. We find that SN feedback operates on all scales from the interstellar medium (ISM) to several virial radii. SNe regulate star formation by preventing condensation of HI into H$_2$ and by moving cold neutral gas to the hot HII phase. The first effect explains why the cold neutral gas in dwarf galaxies forms stars inefficiently. The second maintains the hot ISM of massive galaxies (HII vents out at lower masses). At $v_{\rm vir}\simeq 67{\rm\,km\,s}^{-1}$, the outflow rate follows the relation: $\dot{M}_{\rm out}=23\,(v_{\rm vir}/67{\rm\,km\,s}^{-1})^{-4.6}\,{\rm SFR}$. $20\%$ to $70\%$ of the gas expelled from galaxies escapes from the halo (ejective feedback) but outflows are dominated by cold swept-up gas, most of which falls back onto the galaxy on a $\sim 1\,$Gyr timescale. This `fountain feedback' reduces the masses of galaxies by a factor of two to four, since gas spends half to three quarter of its time in the fountain. Less than $10\%$ of the ejected gas mixes with the hot circumgalactic medium and this gas is usually not reaccreted. On scales as large as $6r_{\rm vir}$, galactic winds divert the incoming gas from cosmic filaments and prevent if from accreting onto galaxies (pre-emptive feedback). This process is the main reason for the low baryon content of ultradwarves.Comment: Submitted for publication in MNRA

Cold-mode and hot-mode accretion in galaxy formation: an entropy approach

We have analysed two cosmological zoom simulations with $M_{\rm vir}\sim 10^{12}{\rm\,M}_\odot$ from the NIHAO series, both with and without feedback. We show that an entropy criterion based on the equation of state of the intergalactic medium can successfully separate cold- and hot-mode accretion. The shock-heated gas has non-negligible turbulent support and cools inefficiently. In the simulations without feedback, only a small fraction ($\sim 20$ per cent) of the stellar mass comes from baryons that have been in the hot circumgalactic medium, although quantitative conclusions should be taken with caution due to our small-number statistics. With feedback, the fraction is larger because of the reaccretion of gas heated by supernovae, which has lower entropies and shorter cooling times than the gas heated by accretion shocks. We have compared the results of NIHAO to predictions of the GalICS 2.1 semianalytic model of galaxy formation. The shock-stability criterion implemented in GalICS 2.1 successfully reproduces the transition from cold- to hot-mode accretion

Higher-order statistics of shear field via a machine learning approach

International audienceContext. The unprecedented amount and the excellent quality of lensing data expected from upcoming ground and space-based surveys present a great opportunity for shedding light on questions that remain unanswered with regard to our universe and the validity of the standard ΛCDM cosmological model. The development of new techniques that are capable of exploiting the vast quantity of data provided by future observations, in the most effective way possible, is of great importance.Aims. This is the reason we chose to investigate the development of a new method for treating weak-lensing higher-order statistics, which are known to break the degeneracy among cosmological parameters thanks to their capacity to probe non-Gaussian properties of the shear field. In particular, the proposed method applies directly to the observed quantity, namely, the noisy galaxy ellipticity.Methods. We produced simulated lensing maps with different sets of cosmological parameters and used them to measure higher-order moments, Minkowski functionals, Betti numbers, and other statistics related to graph theory. This allowed us to construct datasets with a range of sizes, levels of precision, and smoothing. We then applied several machine learning algorithms to determine which method best predicts the actual cosmological parameters associated with each simulation.Results. The most optimal model turned out to be a simple multidimensional linear regression. We use this model to compare the results coming from the different datasets and find that we can measure, with a good level of accuracy, the majority of the parameters considered in this study. We also investigated the relation between each higher-order estimator and the different cosmological parameters for several signal-to-noise thresholds and redshifts bins.Conclusions. Given the promising results we obtained, we consider this approach a valuable resource that is worthy of further development.Key words: gravitational lensing: weak / cosmology: theory / methods: statistica