We present the first estimation of the mass and spin magnitude of Kerr black holes resulting from the coalescence of binary black holes using a deep neural network. The network is trained on a dataset containing 80\% of the full publicly available catalog of numerical simulations of gravitational waves emission by binary black hole systems, including full precession effects for spinning binaries. The network predicts the remnant black holes mass and spin with an error less than 0.04\% and 0.3\% respectively for 90\% of the values in the non-precessing test dataset, it is 0.1\% and 0.3\% respectively in the precessing test dataset. When compared to existing fits in the LIGO algorithm software library, the network enables to reduce the remnant mass root mean square error to one half in the non-precessing case. In the precessing case, both remnant mass and spin mean square errors are decreased to one half, and the network corrects the bias observed in available fits.

中文翻译：

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用深度神经网络预测黑洞合并残余物的性质

我们首次使用深度神经网络对由二元黑洞合并产生的克尔黑洞的质量和自旋幅度进行了估计。该网络在一个数据集上进行训练，该数据集包含 80% 的完整公开可用的双黑洞系统引力波发射数值模拟目录，包括自旋双星的完整进动效应。对于非进动测试数据集中的 90\% 值，网络预测残余黑洞质量和自旋的误差分别小于 0.04\% 和 0.3\%，在非进动测试数据集中分别为 0.1\% 和 0.3\%预处理测试数据集。与 LIGO 算法软件库中的现有拟合相比，该网络能够在非进动情况下将剩余质量均方根误差减少到一半。在处理的情况下，