We test the possibility of using a convolutional neural network to infer the inclination angle of a black hole directly from the incomplete image of the black hole’s shadow in the $uv$-plane. To this end, we develop a proof-of-concept network and use it to explicitly find how the error depends on the degree of coverage, type of input and coverage pattern. We arrive at a typical error of 10° at a level of absolute coverage 1% (for a pattern covering a central part of the $uv$-plane), 0.3% (pattern covering the central part and the periphery, the 0.3% referring to the central part only), and 14% (uniform pattern). These numbers refer to a network that takes both amplitude and phase of the visibility function as inputs. We find that this type of network works best in terms of the error itself and its distribution for different angles. In addition, the same type of network demonstrates similarly good performance on highly blurred images mimicking sources nearing being unresolved. In terms of coverage, the magnitude of the error does not change much as one goes from the central pattern to the uniform one. We argue that this may be due to the presence of a typical scale which can be mostly learned by the network from the central part alone.

我们测试了使用卷积神经网络直接从黑洞中阴影的不完整图像推断黑洞的倾斜角度的可能性。$üv$-飞机。为此，我们开发了一个概念验证网络，并使用它来明确发现错误如何取决于覆盖程度，输入类型和覆盖模式。在绝对覆盖率1％的水平上，我们得出的典型误差为10°（对于覆盖中心区域的图案$üv$-平面），0.3％（覆盖中央部分和外围的图案，0.3％仅指中央部分）和14％（均匀图案）。这些数字指的是将可见度函数的幅度和相位都作为输入的网络。我们发现，就误差本身及其在不同角度的分布而言，这种类型的网络效果最佳。此外，相同类型的网络在模仿源即将解析的高度模糊图像上也表现出类似的良好性能。就覆盖率而言，误差的幅度不会随着从中心模式到均匀模式的变化而变化很大。我们认为这可能是由于存在一个典型的量表，该量表通常可以从网络仅从中心部分获悉。