Galaxy formation and evolution

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.

Abstract:

ABSTRACT In the standard Lambda cold dark matter paradigm, pure dark matter simulations predict dwarf galaxies should inhabit dark matter haloes with a centrally diverging density ‘cusp’. This is in conflict with observations that typically favour a constant density ‘core’. We investigate this ‘cusp-core problem’ in ‘ultra-faint’ dwarf galaxies simulated as part of the ‘Engineering Dwarfs at Galaxy formation’s Edge’ project. We find, similarly to previous work, that gravitational potential fluctuations within the central region of the simulated dwarfs kinematically heat the dark matter particles, lowering the dwarfs’ central dark matter density. However, these fluctuations are not exclusively caused by gas inflow/outflow, but also by impulsive heating from minor mergers. We use the genetic modification approach on one of our dwarf’s initial conditions to show how a delayed assembly history leads to more late minor mergers and, correspondingly, more dark matter heating. This provides a mechanism by which even ultra-faint dwarfs ($M_* \lt 10^5\, \text{M}_{\odot }$), in which star formation was fully quenched at high redshift, can have their central dark matter density lowered over time. In contrast, we find that late major mergers can regenerate a central dark matter cusp, if the merging galaxy had sufficiently little star formation. The combination of these effects leads us to predict significant stochasticity in the central dark matter density slopes of the smallest dwarfs, driven by their unique star formation and mass assembly histories.