Magnetic Confinement, MHD Waves, and Smooth Line Profiles in AGN

In this paper, we show that if the broad line region clouds are in approximate energy equipartition between the magnetic field and gravity, as hypothesized by Rees, there will be a significant effect on the shape and smoothness of broad emission line profiles in active galactic nuclei. Line widths of contributing clouds or flow elements are much wider than their thermal widths, due to the presence of non-dissipative MHD waves, and their collective contribution produce emission line profiles broader and smoother than would be expected if a magnetic field were not present. As an illustration, a simple model of isotropically emitting clouds, normally distributed in velocity, is used to show that smoothness can be achieved for less than 80,000 clouds and may even be as low as a few hundred. We conclude that magnetic confinement has far reaching consequences for observing and modeling active galactic nuclei.Comment: to appear in MNRA

Discrete Point Flow Networks for Efficient Point Cloud Generation

Generative models have proven effective at modeling 3D shapes and their statistical variations. In this paper we investigate their application to point clouds, a 3D shape representation widely used in computer vision for which, however, only few generative models have yet been proposed. We introduce a latent variable model that builds on normalizing flows with affine coupling layers to generate 3D point clouds of an arbitrary size given a latent shape representation. To evaluate its benefits for shape modeling we apply this model for generation, autoencoding, and single-view shape reconstruction tasks. We improve over recent GAN-based models in terms of most metrics that assess generation and autoencoding. Compared to recent work based on continuous flows, our model offers a significant speedup in both training and inference times for similar or better performance. For single-view shape reconstruction we also obtain results on par with state-of-the-art voxel, point cloud, and mesh-based methods.Comment: In ECCV'2

Variational Relational Point Completion Network for Robust 3D Classification

Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise, which hampers 3D geometric modeling and perception. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-made objects. To tackle these challenges, this paper proposes a variational framework, Variational Relational point Completion Network (VRCNet) with two appealing properties: 1) Probabilistic Modeling. In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds. One path consumes complete point clouds for reconstruction by learning a point VAE. The other path generates complete shapes for partial point clouds, whose embedded distribution is guided by distribution obtained from the reconstruction path during training. 2) Relational Enhancement. Specifically, we carefully design point self-attention kernel and point selective kernel module to exploit relational point features, which refines local shape details conditioned on the coarse completion. In addition, we contribute multi-view partial point cloud datasets (MVP and MVP-40 dataset) containing over 200,000 high-quality scans, which render partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD model. Extensive experiments demonstrate that VRCNet outperforms state-of-the-art methods on all standard point cloud completion benchmarks. Notably, VRCNet shows great generalizability and robustness on real-world point cloud scans. Moreover, we can achieve robust 3D classification for partial point clouds with the help of VRCNet, which can highly increase classification accuracy.Comment: 12 pages, 10 figures, accepted by PAMI. project webpage: https://mvp-dataset.github.io/. arXiv admin note: substantial text overlap with arXiv:2104.1015

Unified model for the gamma-ray emission of supernova remnants

Shocks of supernova remnants (SNRs) are important (and perhaps the dominant) agents for production of the Galactic cosmic rays. Recent $\gamma$-ray observations of several SNRs have made this case more compelling. However, these broadband high-energy measurements also reveal a variety of spectral shape demanding more comprehensive modeling of emissions from SNRs. According to the locally observed fluxes of cosmic ray protons and electrons, the electron-to-proton number ratio is known to be about 1%. Assuming such a ratio is universal for all SNRs and identical spectral shape for all kinds of accelerated particles, we propose a unified model that ascribes the distinct $\gamma$-ray spectra of different SNRs to variations of the medium density and the spectral difference between cosmic ray electrons and protons observed at Earth to transport effects. For low density environments, the $\gamma$-ray emission is inverse-Compton dominated. For high density environments like systems of high-energy particles interacting with molecular clouds, the $\gamma$-ray emission is $\pi^0$-decay dominated. The model predicts a hadronic origin of $\gamma$-ray emission from very old remnants interacting mostly with molecular clouds and a leptonic origin for intermediate age remnants whose shocks propagate in a low density environment created by their progenitors via e.g., strong stellar winds. These results can be regarded as evidence in support of the SNR-origin of the Galactic cosmic rays.Comment: 7 pages (two-column), 5 figures, 1 table; discussion added; accepted by Ap