AGN光变曲线非参数建模的条件神经过程
Conditional Neural Process for non-parametric modeling of AGN light curve
摘要：
超大质量黑洞附近复杂扰动环境的后果不能很好地用活动星系核（AGN）光学变率的标准统计模型来表示。因此，开发新的研究和模拟活动星系核光变曲线的方法是至关重要的。条件神经过程（CNP）是一种非线性函数模型，它基于有限数量的已知数据预测随机时间序列，而不使用任何额外的参数或先验知识（核）。我们提供了一个CNP算法，是专门为模拟活动星系核光变曲线。它使用来自超新星全天空自动巡天的数据进行训练，其中包括153个AGN。我们提出了CNP建模性能的子样本的五个活动星系核具有独特的难以建模的属性。采用最小化损失函数对CNP算法预测通量波动的性能进行了评估，结果表明该算法是有效的。我们初步的并行化实验表明，CNP可以有效地处理大量的数据。这些结果意味着CNP在模拟大量活动星系核数据时比标准工具更有效（正如从时域测量中所预期的那样，如Vera C。Rubin Observatory's Legacy Survey of Space and Time）。
英文摘要：
The consequences of complex disturbed environments in the vicinity of a supermassive black hole are not well represented by standard statistical models of optical variability in active galactic nuclei (AGN). Thus, developing new methodologies for investigating and modeling AGN light curves is crucial. Conditional Neural Processes (CNPs) are nonlinear function models that forecast stochastic time-series based on a finite amount of known data without the use of any additional parameters or prior knowledge (kernels). We provide a CNP algorithm that is specifically designed for simulating AGN light curves. It was trained using data from the All-Sky Automated Survey for Supernovae, which included 153 AGN. We present CNP modeling performance for a subsample of five AGNs with distinctive difficult-to-model properties. The performance of CNP in predicting temporal flux fluctuation was assessed using a minimizing loss function, and the results demonstrated the algorithm's usefulness. Our preliminary parallelization experiments show that CNP can efficiently handle large amounts of data. These results imply that CNP can be more effective than standard tools in modeling large volumes of AGN data (as anticipated from time-domain surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time).
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