Diversity of dwarf galaxy IR-submm emission patterns: CLUES from hydrodynamical simulations

Santos-Santos, Isabel M. E.; Domínguez-Tenreiro, Rosa; Granato, Gian Luigi; Brook, C. B.; Obreja, Aura
Bibliographical reference

Astronomy & Astrophysics, Volume 603, id.A4, 22 pp.

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6
2017
Description
Context. The spectral energy distributions (SEDs) of low-mass low-metallicity (dwarf) galaxies are a challenging piece of the puzzle of galaxy formation in the near Universe. These SEDs show some particular features in the submillimeter to far-infrared (FIR) wavelength range compared to normal larger galaxies that cannot be explained by the current models. Aims: We aim to explain the particular emission features of low-mass low-metallicity galaxies in the IR-submm range, which are: a broadening of the IR peak, which implies a warmer dust component; an excess of emission in the submm ( 500 μm), that causes a flattening of the submm/FIR slope; and a very low intensity of polycyclic aromatic hydrocarbon emission features. Methods: The SEDs of a sample of 27 simulated dwarf galaxies were calculated using the GRASIL-3D radiative transfer code. This code has the particularity that it separately treats the radiative transfer through dust grains within molecular clouds and within the cirrus, the dense and diffuse components of the gas phase, respectively. The simulated galaxies have stellar masses ranging from 106-109M⊙, and were obtained from a single simulation run within a Local Group environment with initial conditions from the CLUES project. Results: We report a study of the IRAS, Spitzer, and Herschel bands luminosities, and of the star formation rates, dust, and gas (HI and H2) mass contents. We find a satisfactory agreement with observational data, with GRASIL-3D naturally reproducing the specific spectral features mentioned above. Conclusions: We conclude that the GRASIL-3D two-component dust model gives a physical interpretation of the emission of dwarf galaxies: molecular clouds and cirrus represent the warm and cold dust components, respectively, needed to reproduce observational data.
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