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Register a new userWhat is a GMC? Are Observers and Simulators Discussing the Same Star-forming Clouds?
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As both simulations and observations reach the resolution of the star-forming molecular clouds, it becomes important to clarify if these two techniques are discussing the same objects in galaxies. We compare clouds formed in a high resolution galaxy simulation identified as continuous structures within a contour, in the simulators position-position-position (PPP) co-ordinate space and the observers position-position-velocity space (PPV). Results indicate that the properties of the cloud populations are similar in both methods and up to 70% of clouds have a single counterpart in the opposite data structure. Comparing individual clouds in a one-to-one match reveals a scatter in properties mostly within a factor of two. However, the small variations in mass, radius and velocity dispersion produce significant differences in derived quantities such as the virial parameter. This makes it difficult to determine if a structure is truely gravitationally bound. The three cloud types originally found in the simulation in Fujimoto et al. (2014) are identified in both data sets, with around 80% of the clouds retaining their type between identification methods. We also compared our results when using a peak decomposition method to identify clouds in both PPP and PPV space. The number of clouds increased with this technique, but the overall cloud properties remained similar. However, the more crowded environment lowered the ability to match clouds between techniques to 40%. The three cloud types also became harder to separate, especially in the PPV data set. The method used for cloud identification therefore plays a critical role in determining cloud properties, but both PPP and PPV can potentially identify the same structures.
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To date, most numerical simulations of molecular clouds, and star formation within them, assume a uniform density sphere or box with an imposed turbulent velocity field. In this work, we select molecular clouds from galactic scale simulations as initial conditions, increase their resolution, and re-simulate them using the SPH code Gadget2. Our approach provides clouds with morphologies, internal structures, and kinematics that constitute more consistent and realistic initial conditions for simulations of star formation. We perform comparisons between molecular clouds derived from a galactic simulation, and spheres of turbulent gas of similar dimensions, mass and velocity dispersion. We focus on properties of the clouds such as their density, velocity structure and star formation rate. We find that the inherited velocity structure of the galactic clouds has a significant impact on the star formation rate and evolution of the cloud. Our results indicate that, although we can follow the time evolution of star formation in any simulated cloud, capturing the entire history is difficult as we ignore any star formation that might have occurred before initialisation. Overall, the turbulent spheres do not match the complexity of the galactic clouds.
We present analytic theory for the total column density of singly ionized carbon (C+) in the optically thick photon dominated regions (PDRs) of far-UV irradiated (star-forming) molecular clouds. We derive a simple formula for the C+ column as a function of the cloud (hydrogen) density, the far-UV field intensity, and metallicity, encompassing the wide range of galaxy conditions. When assuming the typical relation between UV and density in the cold neutral medium, the C+ column becomes a function of the metallicity alone. We verify our analysis with detailed numerical PDR models. For optically thick gas, most of the C+ column is mixed with hydrogen that is primarily molecular (H2), and this C+/H2 gas layer accounts for almost all of the `CO-dark molecular gas in PDRs. The C+/H2 column density is limited by dust shielding and is inversely proportional to the metallicity down to ~0.1 solar. At lower metallicities, H2 line blocking dominates and the C+/H2 column saturates. Applying our theory to CO surveys in low redshift spirals we estimate the fraction of C+/H2 gas out of the total molecular gas to be typically ~0.4. At redshifts 1<z<3 in massive disc galaxies the C+/H2 gas represents a very small fraction of the total molecular gas (<0.16). This small fraction at high redshifts is due to the high gas surface densities when compared to local galaxies.
Magnetic fields play such roles in star formation as the angular momentum transport in star-forming clouds, thereby controlling circumstellar disc formation and even binary star formation efficiency. The coupling between the magnetic field and gas is determined by the ionization degree in the gas. Here, we calculate the thermal and chemical evolution of the primordial gas by solving chemical reaction network where all the reactions are reversed. We find that at ~ 10^14-10^18 /cm^3, the ionization degree becomes 100-1000 times higher than the previous results due to the lithium ionization by thermal photons trapped in the cloud, which has been omitted so far. We construct the minimal chemical network which can reproduce correctly the ionization degree as well as the thermal evolution by extracting 36 reactions among 13 species. Using the obtained ionization degree, we evaluate the magnetic field diffusivity. We find that the field dissipation can be neglected for global fields coherent over > a tenth of the cloud size as long as the field is not so strong as to prohibit the collapse. With magnetic fields strong enough for ambipolar diffusion heating to be significant, the magnetic pressure effects to slow down the collapse and to reduce the compressional heating become more important, and the temperature actually becomes lower than in the no-field case.
We study the star formation (SF) law in 12 Galactic molecular clouds with ongoing high-mass star formation (HMSF) activity, as traced by the presence of a bright IRAS source and other HMSF tracers. We define the molecular cloud (MC) associated to each IRAS source using 13CO line emission, and count the young stellar objects (YSOs) within these clouds using GLIMPSE and MIPSGAL 24 micron Spitzer databases.The masses for high luminosity YSOs (Lbol>10~Lsun) are determined individually using Pre Main Sequence evolutionary tracks and the evolutionary stages of the sources, whereas a mean mass of 0.5 Msun was adopted to determine the masses in the low luminosity YSO population. The star formation rate surface density (sigsfr) corresponding to a gas surface density (siggas) in each MC is obtained by counting the number of the YSOs within successive contours of 13CO line emission. We find a break in the relation between sigsfr and siggas, with the relation being power-law (sigsfr ~ siggas^N) with the index N varying between 1.4 and 3.6 above the break. The siggas at the break is between 150-360 Msun/pc^2 for the sample clouds, which compares well with the threshold gas density found in recent studies of Galactic star-forming regions. Our clouds treated as a whole lie between the Kennicutt (1998) relation and the linear relation for Galactic and extra-galactic dense star-forming regions. We find a tendency for the high-mass YSOs to be found preferentially in dense regions at densities higher than 1200 Msun/pc^2 (~0.25 g/cm^2).
A model of magnetic field structure is presented to help test the prevalence of flux freezing in star-forming clouds of various shapes, orientations, and degrees of central concentration, and to estimate their magnetic field strength. The model is based on weak-field flux freezing in centrally condensed Plummer spheres and spheroids of oblate and prolate shape. For a spheroid of given density contrast, aspect ratio, and inclination, the model estimates the local field strength and direction, and the global field pattern of hourglass shape. Comparisons with a polarization simulation indicate typical angle agreement within 1 - 10 degrees. Scalable analytic expressions are given to match observed polarization patterns, and to provide inputs to radiative transfer codes for more accurate predictions. The model may apply to polarization observations of dense cores, elongated filamentary clouds, and magnetized circumstellar disks.
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