Astrophysical images in the GeV band are challenging to analyze due to the strong contribution of the background and foreground astrophysical diffuse emission and relatively broad point spread function of modern space-based instruments. In certain cases, even finding of point sources on the image becomes a non-trivial task. We present a method for point sources extraction using a convolution neural network (CNN) trained on our own artificial data set which imitates images from the Fermi Large Area Telescope. These images are raw count photon maps of 10x10 degrees covering energies from 1 to 10 GeV. We compare different CNN architectures that demonstrate accuracy increase by ~15% and reduces the inference time by at least the factor of 4 accuracy improvement with respect to a similar state of the art models.

中文翻译：

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模拟 Fermi/LAT 望远镜影像的神经网络点源提取研究

由于现代天基仪器的背景和前景天体物理漫发射和相对较宽的点扩散函数的强大贡献，GeV 波段的天体物理图像难以分析。在某些情况下，甚至在图像上查找点源也变得非常重要。我们提出了一种使用卷积神经网络 (CNN) 提取点源的方法，该网络在我们自己的人工数据集上训练，模仿来自费米大面积望远镜的图像。这些图像是 10x10 度的原始计数光子图，涵盖了 1 到 10 GeV 的能量。我们比较了不同的 CNN 架构，这些架构证明准确度提高了约 15%，并将推理时间减少了至少 4 倍，相对于类似的最先进模型而言，准确度提高了 4 倍。