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Register a new userThe Evolving Structure of Galactic Disks
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Hugo Martel
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Observations suggest that the structural parameters of disk galaxies have not changed greatly since redshift 1. We examine whether these observations are consistent with a cosmology in which structures form hierarchically. We use SPH/N-body galaxy-scale simulations to simulate the formation and evolution of Milky-Way-like disk galaxies by fragmentation, followed by hierarchical merging. The simulated galaxies have a thick disk, that forms in a period of chaotic merging at high redshift, during which a large amount of alpha-elements are produced, and a thin disk, that forms later and has a higher metallicity. Our simulated disks settle down quickly and do not evolve much since redshift z~1, mostly because no major mergers take place between z=1 and z=0. During this period, the disk radius increases (inside-out growth) while its thickness remains constant. These results are consistent with observations of disk galaxies at low and high redshift.
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Surface-brightness profiles for early-type (S0-Sb) disks exhibit three main classes (Type I, II, and III). Type II profiles are more common in barred galaxies, and most of the time appear to be related to the bars Outer Lindblad Resonance. Roughly half of barred galaxies in the field have Type II profiles, but almost none in the Virgo Cluster do; this might be related to ram-pressure stripping in clusters. A strong textit{anti}correlation is found between Type III profiles (antitruncations) and bars: Type III profiles are most common when there is no bar, and least common when there is a strong bar.
The spatial distribution of the HI gas in galaxies holds important clues on the physical processes that shape the structure and dynamics of the interstellar medium (ISM). In this work, we quantify the structure of the HI gas in a sample of 33 nearby galaxies taken from the THINGS Survey using the delta-variance spectrum. The THINGS galaxies display a large diversity in their spectra, however, there are a number of recurrent features. In many galaxies, we observe a bump in the spectrum on scales of a few to several hundred pc. We find the characteristic scales associated with the bump to be correlated with galactic SFR for values of the SFR > 0.5 M$_{sol}$ yr$^{-1}$ and also with the median size of the HI shells detected in those galaxies. On larger scales, we observe the existence of two self-similar regimes. The first one, on intermediate scales is shallow and the power law that describes this regime has an exponent in the range [0.1-1] with a mean value of 0.55 which is compatible with the density field being generated by supersonic turbulence in the cold phase of the HI gas. The second power law is steeper, with a range of exponents between [0.5-1.5] and a mean value of 1.5. These values are associated with subsonic turbulence which is characteristic of the warm phase of the HI gas. The spatial scale at which the transition between the two regimes occurs is found to be $approx 0.5 R_{25}$ which is similar to the size of the molecular disk in the THINGS galaxies. Overall, our results suggest that on scales < $0.5 R_{25}$, the structure of the ISM is affected by the effects of supernova explosions. On larger scales (> 0.5 $R_{25}$), stellar feedback has no significant impact, and the structure of the ISM is determined by large scale processes that govern the dynamics of the gas in the warm neutral medium such as the flaring of the HI disk and the effects of ram pressure stripping.
Numerical simulations of planet-disk interactions are usually performed with hydro-codes that -- because they consider only an annulus of the disk, over a 2D grid -- can not take into account the global evolution of the disk. However, the latter governs planetary migration of type II, so that the accuracy of the planetary evolution can be questioned. To develop an algorithm that models the local planet-disk interactions together with the global viscous evolution of the disk, we surround the usual 2D grid with a 1D grid ranging over the real extension of the disk. The 1D and 2D grids are coupled at their common boundaries via ghost rings, paying particular attention to the fluxes at the interface, especially the flux of angular momentum carried by waves. The computation is done in the frame centered on the center of mass to ensure angular momentum conservation. The global evolution of the disk and the local planet-disk interactions are both well described and the feedback of one on the other can be studied with this algorithm, for a negligible additional computing cost with respect to usual algorithms.
This paper develops a general framework for learning interpretable data representation via Long Short-Term Memory (LSTM) recurrent neural networks over hierarchal graph structures. Instead of learning LSTM models over the pre-fixed structures, we propose to further learn the intermediate interpretable multi-level graph structures in a progressive and stochastic way from data during the LSTM network optimization. We thus call this model the structure-evolving LSTM. In particular, starting with an initial element-level graph representation where each node is a small data element, the structure-evolving LSTM gradually evolves the multi-level graph representations by stochastically merging the graph nodes with high compatibilities along the stacked LSTM layers. In each LSTM layer, we estimate the compatibility of two connected nodes from their corresponding LSTM gate outputs, which is used to generate a merging probability. The candidate graph structures are accordingly generated where the nodes are grouped into cliques with their merging probabilities. We then produce the new graph structure with a Metropolis-Hasting algorithm, which alleviates the risk of getting stuck in local optimums by stochastic sampling with an acceptance probability. Once a graph structure is accepted, a higher-level graph is then constructed by taking the partitioned cliques as its nodes. During the evolving process, representation becomes more abstracted in higher-levels where redundant information is filtered out, allowing more efficient propagation of long-range data dependencies. We evaluate the effectiveness of structure-evolving LSTM in the application of semantic object parsing and demonstrate its advantage over state-of-the-art LSTM models on standard benchmarks.
We present the analysis of a CCD survey of 31 nearby (<= 110 Mpc) edge-on spiral galaxies. The three-dimensional one-component best fit models provide their disk-scalelengths h and for the first time their disk cut-off radii R_{co}. We confirm for this sample the existence of such sharp truncations, and find a significantly lower mean value of the distance independent ratio R_{co}/h =2.9 +- 0.7 than the standard value of 4.5 often used in the literature. Our data show no correlation of these parameters with Hubble type, whereas we report a correlation between R_{co}/h and the distance based scalelength in linear units. Compared to the Milky Way we find only lower values of R_{co}/h, explained either by possible selection effects or by the completely different techniques used. We discuss our data in respect to present models for the origin of the cut-off radii, either as a relict of the galaxy formation process, or as an evolutionary phenomenon.
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