论文标题
Agnet：用机器学习称重黑洞
AGNet: Weighing Black Holes with Machine Learning
论文作者
论文摘要
超大型黑洞（SMBH）在大多数星系中心都普遍存在。测量SMBH质量对于理解SMBH的起源和演变很重要。但是，传统方法需要昂贵收集的光谱数据。为了解决这个问题，我们提出了一种使用Quasar Light Time系列称重SMBH的算法，从而规定了对昂贵光谱的需求。我们训练，验证和测试神经网络，这些神经网络直接从斯隆数字天空调查（SDSS）条纹中学习82个数据，以$ 9,038 $光谱镜确认的类星体的样本，以绘制黑洞质量和多色光学光曲线之间的非线性编码。我们发现，基于SDSS单两个光谱的预测质量和基准病毒质量之间的1 $σ$散射为0.35 DEX。我们的结果对有效的应用有直接的影响，其中包括Vera Rubin天文台的未来观察结果。
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.
