AGNet: weighing black holes with deep learning
AGNet: weighing black holes with deep learning
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AGNet：用深度学习衡量黑洞的重量
DOI：
10.1093/mnras/stac3339
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发表时间：
2022
影响因子：
4.8
通讯作者：
Kindratenko, Volodymyr
中科院分区：
文献类型：
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作者：
Lin, Joshua Yao-Yu;Pandya, Sneh;Pratap, Devanshi;Liu, Xin;Carrasco Kind, Matias;Kindratenko, Volodymyr
超大质量黑洞（SMBHs）通常在大多数大质量星系的中心发现。测量SMBH的质量对于理解SMBH的起源和演化至关重要。另一方面，传统的方法需要收集光谱数据，这是昂贵的。我们提出了一种利用类星体光时间序列信息（包括颜色、多波段星等和光曲线的可变性）对SMBHs进行加权的算法，从而避免了对昂贵光谱的需要。我们训练、验证和测试了直接学习斯隆数字巡天（SDSS）条纹82光曲线的神经网络，用于38939个光谱确认的类星体样本，以绘制SMBH质量和多波段光学光曲线之间的非线性编码。我们发现，基于SDSS单历元光谱的SMBH预测质量与基准维里质量估计值之间存在0.37指数的1σ散射，这与维里质量估计值中的系统不确定性相当。我们的结果对Vera C. Rubin天文台未来更有效的观测有直接的影响。我们的代码AGNet可以在https://github.com/snehjp2/AGNet上公开获得。
Supermassive black holes (SMBHs) are commonly found at the centres of most massive galaxies. Measuring SMBH mass is crucial for understanding the origin and evolution of SMBHs. Traditional approaches, on the other hand, necessitate the collection of spectroscopic data, which is costly. We present an algorithm that weighs SMBHs using quasar light time series information, including colours, multiband magnitudes, and the variability of the light curves, 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 light curves for a sample of 38 939 spectroscopically confirmed quasars to map out the non-linear encoding between SMBH mass and multiband optical light curves. We find a 1σ scatter of 0.37 dex between the predicted SMBH mass and the fiducial virial mass estimate based on SDSS single-epoch spectra, which is comparable to the systematic uncertainty in the virial mass estimate. Our results have direct implications for more efficient applications with future observations from the Vera C. Rubin Observatory. Our code,AGNet, is publicly available at https://github.com/snehjp2/AGNet.