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The upcoming next-generation large area radio continuum surveys can expect tens of millions of radio sources, rendering the traditional method for radio morphology classification through visual inspection unfeasible. We present ClaRAN - Classifying Radio sources Automatically with Neural networks - a proof-of-concept radio source morphology classifier based upon the Faster Region-based Convolutional Neutral Networks (Faster R-CNN) method. Specifically, we train and test ClaRAN on the FIRST and WISE images from the Radio Galaxy Zoo Data Release 1 catalogue. ClaRAN provides end users with automated identification of radio source morphology classifications from a simple input of a radio image and a counterpart infrared image of the same region. ClaRAN is the first open-source, end-to-end radio source morphology classifier that is capable of locating and associating discrete and extended components of radio sources in a fast (< 200 milliseconds per image) and accurate (>= 90 %) fashion. Future work will improve ClaRAN's relatively lower success rates in dealing with multi-source fields and will enable ClaRAN to identify sources on much larger fields without loss in classification accuracy., 22 pages, 16 figures, Accepted in Monthly Notices of the Royal Astronomical Society

Inspired on the well known dynamical dichotomy predicted in voids, where some underdense regions expand whereas others collapse due to overdense surrounding regions, we explored the interplay between the void inner dynamics and its large scale environment. The environment is classified depending on its density as in previous works. We analyse the dynamical properties of void-centered spherical shells at different void-centric distances depending on this classification. The above dynamical properties are given by the angular distribution of the radial velocity field, its smoothness, the field dependence on the tracer density and shape, and the field departures from linear theory. We found that the velocity field in expanding voids follows more closely the linear prediction, with a more smooth velocity field. However when using velocity tracers with large densities such deviations increase. Voids with sizes around $18\,h^{-1}\,Mpc$ are in a transition regime between regions with expansion overpredicted and underpredicted from linear theory. We also found that velocity smoothness increases as the void radius, indicating the laminar flow dominates the expansion of larger voids (more than $18\,h^{-1}\,Mpc$). The correlations observed suggest that nonlinear dynamics of the inner regions of voids could be dependent on the evolution of the surrounding structures. These also indicate possible scale couplings between the void inner expansion and the large scale regions where voids are embedded. These results shed some light to the origin of nonlinearities in voids, going beyond the fact that voids just quickly becomes nonlinear as they become emptier., Comment: 12 pages, 8 Figures, accepted for publication in MNRAS