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The relative orientation between filamentary structures in molecular clouds and the ambient magnetic field provides insight into filament formation and stability. To calculate the relative orientation, a measurement of filament orientation is first required. We propose a new method to calculate the orientation of the one pixel wide filament skeleton that is output by filament identification algorithms such as textsc{filfinder}. We derive the local filament orientation from the direction of the intensity gradient in the skeleton image using the Sobel filter and a few simple post-processing steps. We call this the `Sobel-gradient method. The resulting filament orientation map can be compared quantitatively on a local scale with the magnetic field orientation map to then find the relative orientation of the filament with respect to the magnetic field at each point along the filament. It can also be used in constructing radial profiles for filament width fitting. The proposed method facilitates automation in analysis of filament skeletons, which is imperative in this era of `big data.
This study implements a vector space model approach to measure the sentiment orientations of words. Two representative vectors for positive/negative polarity are constructed using high-dimensional vec-tor space in both an unsupervised and a semi-supe
Observations suggest that satellite quenching plays a major role in the build-up of passive, low-mass galaxies at late cosmic times. Studies of low-mass satellites, however, are limited by the ability to robustly characterize the local environment an
We perform simulations to test the effects of a moving gas filament on a young star cluster (i.e. the Slingshot Model). We model Orion Nebula Cluster-like clusters as Plummer spheres and the Integral Shaped Filament gas as a cylindrical potential. We
Observations of the Orion-A integral shaped filament (ISF) have shown indications of an oscillatory motion of the gas filament. This evidence is based on both the wave-like morphology of the filament as well as the kinematics of the gas and stars, wh
Parameter estimation with non-Gaussian stochastic fields is a common challenge in astrophysics and cosmology. In this paper, we advocate performing this task using the scattering transform, a statistical tool sharing ideas with convolutional neural n