Galaxy formation and evolution

Abstract:

We present high spatial resolution (≈12 pc) Atacama Large Millimeter/submillimeter Array 12CO(J = 3–2) observations of the nearby lenticular galaxy NGC 4429. We identify 217 giant molecular clouds within the 450 pc radius molecular gas disc. The clouds generally have smaller sizes and masses but higher surface densities and observed linewidths than those of Milky Way disc clouds. An unusually steep size–linewidth relation ($\sigma \propto R_{\rm c}^{0.8}$) and large cloud internal velocity gradients (0.05–0.91 km s−1 pc−1) and observed virial parameters (〈αobs,vir〉 ≈ 4.0) are found, which appear due to internal rotation driven by the background galactic gravitational potential. Removing this rotation, an internal virial equilibrium appears to be established between the self-gravitational (Usg) and turbulent kinetic (Eturb) energies of each cloud, i.e. $\langle \alpha _{\rm sg,vir}\equiv \frac{2E_{\rm turb}}{\vert U_{\rm sg}\vert }\rangle \approx 1.3$. However, to properly account for both self and external gravity (shear and tidal forces), we formulate a modified virial theorem and define an effective virial parameter $\alpha _{\rm eff,vir}\equiv \alpha _{\rm sg,vir}+\frac{E_{\rm ext}}{\vert U_{\rm sg}\vert }$ (and associated effective velocity dispersion). The NGC 4429 clouds then appear to be in a critical state in which the self-gravitational energy and the contribution of external gravity to the cloud’s energy budget (Eext) are approximately equal, i.e. $\frac{E_{\rm ext}}{\vert U_{\rm sg}\vert }\approx 1$. As such, 〈αeff,vir〉 ≈ 2.2 and most clouds are not virialized but remain marginally gravitationally bound. We show this is consistent with the clouds having sizes similar to their tidal radii and being generally radially elongated. External gravity is thus as important as self-gravity to regulate the clouds of NGC 4429.

Abstract:

Large galaxy samples from multi-object IFS surveys now allow for a statistical analysis of the z~0 galaxy population using resolved kinematics. However, the improvement in number statistics comes at a cost, with multi-object IFS surveys more severely impacted by the effect of seeing and lower signal-to-noise. We present an analysis of ~1800 galaxies from the SAMI Galaxy Survey and investigate the spread and overlap in the kinematic distributions of the spin parameter proxy $\lambda_{Re}$ as a function of stellar mass and ellipticity. For SAMI data, the distributions of galaxies identified as regular and non-regular rotators with $kinemetry$ show considerable overlap in the $\lambda_{Re}$-$\varepsilon_e$ diagram. In contrast, visually classified galaxies (obvious and non-obvious rotators) are better separated in $\lambda_{Re}$ space, with less overlap of both distributions. Then, we use a Bayesian mixture model to analyse the $\lambda_{Re}$-$\log(M_*/M_{\odot})$ distribution. As a function of mass, we investigate whether the data are best fit with a single kinematic distribution or with two. Below $\log(M_*/M_{\odot})$~10.5 a single beta distribution is sufficient to fit the complete $\lambda_{Re}$ distribution, whereas a second beta distribution is required above $\log(M_*/M_{\odot})$~10.5 to account for a population of low-$\lambda_{Re}$ galaxies, presenting the cleanest separation of the two populations. We apply the same analysis to mock-observations from cosmological simulations. The mixture model predicts a bimodal $\lambda_{Re}$ distribution for all simulations, albeit with different positions of the $\lambda_{Re}$ peaks and with different ratios of both populations. Our analysis validates the conclusions from previous, smaller IFS surveys, but also demonstrates the importance of using kinematic selection criteria that are dictated by the quality of the observed or simulated data.

Abstract:

High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics—however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. From a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis.