SearcharxivSearch

arXiv subjects

Yiyang Zeng

Publications and source records attributed to Yiyang Zeng.

2 recordsLinked to original sources

22 Newly Identified Repeating Changing-look AGNs and Evidence for Extreme Broad-line Region Breathing

Changing-look active galactic nuclei (CL AGNs) show the appearance or disappearance of broad emission lines on timescales of years. Among them, the repeating CL (RCL) AGNs may provide a clear clue for our understanding of the CL transitions because the same nucleus crosses some physical boundaries more than once. We search for RCL AGNs in known CL-AGN samples using long-term multi-band light curves, and selected 34 candidates for spectroscopic follow-up. We confirm 25 RCL AGNs, including 22 newly identified cases. Properties of these RCL AGNs are analyzed. The observed rest-frame intervals of the second transitions are mostly 3--4 yr, while the variations of the optical light curves suggest that some transitions might occur on timescales of several months. The latest spectra show that the on/off states correspond to higher/lower Eddington-ratio, in the expected direction relative to the parent CL-AGN samples. As seven RCL AGNs are well covered by nearly continuous single-band light curves, their on/off states can be found to follow multi-year optical excursions, and their Eddington ratios vary consistently with the photometric changes. We also find that the H$\beta$-only transitions occur at higher Eddington ratios than the transitions involving both H$\alpha$ and H$\beta$, suggesting a line-dependent Broad-Line-Region (BLR) visibility threshold. These results support a picture in which different accretion-flow processes drive reversible changes in the central ionizing emissions, while the observed RCL transitions are produced by the BLR breathing across line-dependent visibility thresholds.

astro-ph.GA

Accurate Trajectory Prediction for Autonomous Vehicles

Predicting vehicle trajectories, angle and speed is important for safe and comfortable driving. We demonstrate the best predicted angle, speed, and best performance overall winning the top three places of the ICCV 2019 Learning to Drive challenge. Our key contributions are (i) a general neural network system architecture which embeds and fuses together multiple inputs by encoding, and decodes multiple outputs using neural networks, (ii) using pre-trained neural networks for augmenting the given input data with segmentation maps and semantic information, and (iii) leveraging the form and distribution of the expected output in the model.

cs.CV