论文标题
对模拟费米/LAT望远镜图像的神经网络点源提取的研究
A study of Neural networks point source extraction on simulated Fermi/LAT Telescope images
论文作者
论文摘要
GEV频段中的天体物理图像由于背景和前景天体物理弥漫性发射以及现代空间基仪器的相对较宽的点传播功能而在分析方面具有挑战性。在某些情况下，即使在图像上发现点源也成为一项非平凡的任务。我们提出了一种使用我们自己的人工数据集训练的卷积神经网络（CNN）提取点源提取的方法，该数据模仿了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，覆盖1至10 GEV的能量。我们比较了不同的CNN体系结构，这些体系结构表明准确性增加了约15％，并将推理时间降低至少相对于类似的TAR模型，至少提高了4个精度的提高因子。
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.
