Galaxy formation and evolution

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.

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.