论文标题

部分可观测时空混沌系统的无模型预测

A machine learning approach to assessing the presence of substructure in quasar host galaxies using the Hyper Suprime-Cam Subaru Strategic Program

论文作者

Nagele, Chris, Silverman, John D., Hartwig, Tilman, Li, Junyao, Bottrell, Connor, Ding, Xuheng, Toba, Yoshiki

论文摘要

储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。

The conditions under which galactic nuclear regions become active are largely unknown, although it has been hypothesized that secular processes related to galaxy morphology could play a significant role. We investigate this question using optical i-band images of 3096 SDSS quasars and galaxies at 0.3<z<0.6 from the Hyper Suprime-Cam Subaru Strategic Program, which possess a unique combination of area, depth and resolution, allowing the use of residual images, after removal of the quasar and smooth galaxy model, to investigate internal structural features. We employ a variational auto-encoder which is a generative model that acts as a form of dimensionality reduction. We analyze the lower dimensional latent space in search of features which correlate with nuclear activity. We find that the latent space does separate images based on the presence of nuclear activity which appears to be associated with more pronounced components (i.e., arcs, rings and bars) as compared to a matched control sample of inactive galaxies. These results suggest the importance of secular processes, and possibly mergers (by their remnant features) in activating or sustaining black hole growth. Our study highlights the breadth of information available in ground-based imaging taken under optimal seeing conditions and having accurate characterization of the point spread function (PSF) thus demonstrating future science to come from the Rubin Observatory.

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