AGNet：用机器学习衡量黑洞
AGNet: Weighing Black Holes with Machine Learning
摘要：
超大质量黑洞（SMBH）在大多数星系的中心无处不在。测量SMBH质量对于理解SMBH的起源和演化非常重要。然而，传统方法需要光谱数据，收集成本高昂。为了解决这个问题，我们提出了一种算法，使用类星体光时间序列来衡量SMBHs，从而避免了对昂贵光谱的需求。我们训练，验证和测试神经网络，直接从斯隆数字巡天（SDSS）条纹82数据的样本$9,038$光谱确认类星体映射出黑洞质量和多色光学光变曲线之间的非线性编码。我们发现一个1 $σ$分散0.35 dex之间的预测质量和基准维里质量的基础上SDSS单历元光谱。我们的研究结果有直接的影响，有效的应用与未来的观测从维拉鲁宾天文台。
英文摘要：
Supermassive black holes (SMBHs) are ubiquitously found at the centers of most galaxies. Measuring SMBH mass is important for understanding the origin and evolution of SMBHs. However, traditional methods require spectral data which is expensive to gather. To solve this problem, we present an algorithm that weighs SMBHs using quasar light time series, circumventing the need for expensive spectra. We train, validate, and test neural networks that directly learn from the Sloan Digital Sky Survey (SDSS) Stripe 82 data for a sample of $9,038$ spectroscopically confirmed quasars to map out the nonlinear encoding between black hole mass and multi-color optical light curves. We find a 1$σ$ scatter of 0.35 dex between the predicted mass and the fiducial virial mass based on SDSS single-epoch spectra. Our results have direct implications for efficient applications with future observations from the Vera Rubin Observatory.
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