Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes
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发表时间：
2021-06
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通讯作者：
Ji Won Park;A. Villar;Yin Li;Yan-Fei Jiang;S. Ho;J. Lin;P. Marshall;A. Roodman
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作者：
Ji Won Park;A. Villar;Yin Li;Yan-Fei Jiang;S. Ho;J. Lin;P. Marshall;A. Roodman
在宇宙中最极端的物体中，活动星系核（AGN）是星系的发光中心，黑洞以周围的物质为食。活动星系核发出的光的变化模式包含了有关黑洞物理性质的信息。即将到来的望远镜将在多个宽带波长下观测超过1亿个活动星系核，产生大量具有长间隔和不规则采样的多元时间序列样本。我们提出了一种方法，重建活动星系核的时间序列，同时推断后验概率密度分布（PDF）的物理量的黑洞，包括它的质量和光度。我们将这种方法应用于11，000 AGN的模拟数据集，并报告推断黑洞质量的精度和准确度为0.4 dex和0.3 dex。这项工作是第一个解决概率时间序列重建和参数推断AGN在端到端的方式。
Among the most extreme objects in the Universe, active galactic nuclei (AGN) are luminous centers of galaxies where a black hole feeds on surrounding matter. The variability patterns of the light emitted by an AGN contain information about the physical properties of the underlying black hole. Upcoming telescopes will observe over 100 million AGN in multiple broadband wavelengths, yielding a large sample of multivariate time series with long gaps and irregular sampling. We present a method that reconstructs the AGN time series and simultaneously infers the posterior probability density distribution (PDF) over the physical quantities of the black hole, including its mass and luminosity. We apply this method to a simulated dataset of 11,000 AGN and report precision and accuracy of 0.4 dex and 0.3 dex in the inferred black hole mass. This work is the first to address probabilistic time series reconstruction and parameter inference for AGN in an end-to-end fashion.