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:

Abstract We are standing at the crossroads of powerful new facilities emerging in the next decade on the ground and in space like ELT, SKA, JWST, and Athena. Turning the narrative of the star formation potential of galaxies into a quantitative theory will provide answers to many outstanding questions in astrophysics, from the formation of planets to the evolution of galaxies and the origin of heavy elements. To achieve this goal, there is an urgent need for a dedicated space-borne, far-infrared spectroscopic facility capable of delivering, for the first time, large scale, high spectral resolution (velocity resolved) multiwavelength studies of the chemistry and dynamics of the ISM of our own Milky Way and nearby galaxies. The Far Infrared Spectroscopic Surveyor (FIRSS) fulfills these requirements and by exploiting the legacy of recent photometric surveys it seizes the opportunity to shed light on the fundamental building processes of our Universe.

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:

We present a comparative study of four physical dust models and two single-temperature modified blackbody models by fitting them to the resolved WISE, Spitzer, and Herschel photometry of M101 (NGC 5457). Using identical data and a grid-based fitting technique, we compare the resulting dust and radiation field properties derived from the models. We find that the dust mass yielded by the different models can vary by up to a factor of 3 (factor of 1.4 between physical models only), although the fits have similar quality. Despite differences in their definition of the carriers of the mid-IR aromatic features, all physical models show the same spatial variations for the abundance of that grain population. Using the well-determined metallicity gradient in M101 and resolved gas maps, we calculate an approximate upper limit on the dust mass as a function of radius. All physical dust models are found to exceed this maximum estimate over some range of galactocentric radii. We show that renormalizing the models to match the same Milky Way high-latitude cirrus spectrum and abundance constraints can reduce the dust mass differences between models and bring the total dust mass below the maximum estimate at all radii.