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Dongwei Bao

Publications and source records attributed to Dongwei Bao.

2 recordsLinked to original sources

AGN STORM 2: I. First results: A Change in the Weather of Mrk 817

We present the first results from the ongoing, intensive, multi-wavelength monitoring program of the luminous Seyfert 1 galaxy Mrk 817. While this AGN was, in part, selected for its historically unobscured nature, we discovered that the X-ray spectrum is highly absorbed, and there are new blueshifted, broad and narrow UV absorption lines, which suggest that a dust-free, ionized obscurer located at the inner broad line region partially covers the central source. Despite the obscuration, we measure UV and optical continuum reverberation lags consistent with a centrally illuminated Shakura-Sunyaev thin accretion disk, and measure reverberation lags associated with the optical broad line region, as expected. However, in the first 55 days of the campaign, when the obscuration was becoming most extreme, we observe a de-coupling of the UV continuum and the UV broad emission line variability. The correlation recovers in the next 42 days of the campaign, as Mrk 817 enters a less obscured state. The short CIV and Ly alpha lags suggest that the accretion disk extends beyond the UV broad line region.

astro-ph.HE

An Automatic Design Framework of Swarm Pattern Formation based on Multi-objective Genetic Programming

Most existing swarm pattern formation methods depend on a predefined gene regulatory network (GRN) structure that requires designers' priori knowledge, which is difficult to adapt to complex and changeable environments. To dynamically adapt to the complex and changeable environments, we propose an automatic design framework of swarm pattern formation based on multi-objective genetic programming. The proposed framework does not need to define the structure of the GRN-based model in advance, and it applies some basic network motifs to automatically structure the GRN-based model. In addition, a multi-objective genetic programming (MOGP) combines with NSGA-II, namely MOGP-NSGA-II, to balance the complexity and accuracy of the GRN-based model. In evolutionary process, an MOGP-NSGA-II and differential evolution (DE) are applied to optimize the structures and parameters of the GRN-based model in parallel. Simulation results demonstrate that the proposed framework can effectively evolve some novel GRN-based models, and these GRN-based models not only have a simpler structure and a better performance, but also are robust to the complex and changeable environments.

cs.NE