用深度学习估算28万黑洞质量，精度逼近观测金标准
287,872 Supermassive Black Holes Masses: Deep Learning Approaching Reverberation Mapping Accuracy
- 用深度编码解码网络分析光谱，替代传统经验公式
- 误差仅0.058dex，对大小黑洞都保持高精度
- 适合需要大规模精确黑洞质量的研究者
我们构建了一个包含287,872个超大质量黑洞质量的规模性目录，精度极高。通过在849个类星体的光学光谱上训练深度编码解码网络（以时间延迟测量为基础的黑洞质量标签），并应用于所有红移z≤4的SDSS类星体，该方法实现了0.058 dex的均方根误差、约14%的相对不确定性以及R²≈0.91的决定系数，显著优于传统单线维里估计器。尤其值得注意的是，该方法在低质量（<10⁷·⁵ M☉）和高质量（>10⁹ M☉）类星体中仍保持高精度，而这些区域的传统经验关系已不可靠。
原文摘要 · Abstract (English)
We present a population-scale catalogue of 287,872 supermassive black hole masses with high accuracy. Using a deep encoder-decoder network trained on optical spectra with reverberation-mapping (RM) based labels of 849 quasars and applied to all SDSS quasars up to $z=4$, our method achieves a root-mean-square error of $0.058$\,dex, a relative uncertainty of $\approx 14\%$, and coefficient of determination $R^{2}\approx0.91$ with respect to RM-based masses, far surpassing traditional single-line virial estimators. Notably, the high accuracy is maintained for both low ($<10^{7.5}\,M_\odot$) and high ($>10^{9}\,M_\odot$) mass quasars, where empirical relations are unreliable.
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