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arXiv 2109.09077astro-ph.IMastro-ph.GAcs.CVcs.LG
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DECORAS:使用深度学习检测和表征射电天文源

DECORAS: detection and characterization of radio-astronomical sources using deep learning

University of Groningen(格罗宁根大学);ASTRON, Netherlands Institute for Radio Astronomy(荷兰射电天文研究所);University of Birmingham(伯明翰大学);Guangzhou University(广州大学)

S. Rezaei, J. P. McKean, M. Biehl, A. Javadpour

摘要:

我们提出了DECORAS,一种基于深度学习的方法,用于从甚长基线干涉测量(VLBI)观测中检测点源和扩展源。我们的方法基于编码器-解码器神经网络架构,该架构使用少量的卷积层来为源检测提供可扩展的解决方案。此外,DECORAS还根据探测到的光源的位置、有效半径和峰值亮度进行光源表征。我们已经训练和测试了网络,图像基于20 cm处的现实甚长基线阵列(VLBA)观测。此外,这些图像没有经过任何先前的去卷积步骤,并且经由傅立叶变换与可见度数据直接相关。我们发现,由DECORAS产生的源目录有一个更好的整体完整性和纯度相比,传统的源检测算法。DECORAS在7.5 $σ$水平下完成,在5.5 $σ$水平下可靠性几乎提高2倍。我们发现,DECORAS可以恢复检测到的源的位置在0.61 $\pm$ 0.69质量,和有效半径和峰值表面亮度恢复到20%以内的98%和94%的来源,分别。总的来说,我们发现,DECORAS提供了一个可靠的源检测和表征解决方案,为未来的宽场VLBI调查。

英文摘要:

We present DECORAS, a deep learning based approach to detect both point and extended sources from Very Long Baseline Interferometry (VLBI) observations. Our approach is based on an encoder-decoder neural network architecture that uses a low number of convolutional layers to provide a scalable solution for source detection. In addition, DECORAS performs source characterization in terms of the position, effective radius and peak brightness of the detected sources. We have trained and tested the network with images that are based on realistic Very Long Baseline Array (VLBA) observations at 20 cm. Also, these images have not gone through any prior de-convolution step and are directly related to the visibility data via a Fourier transform. We find that the source catalog generated by DECORAS has a better overall completeness and purity, when compared to a traditional source detection algorithm. DECORAS is complete at the 7.5$σ$ level, and has an almost factor of two improvement in reliability at 5.5$σ$. We find that DECORAS can recover the position of the detected sources to within 0.61 $\pm$ 0.69 mas, and the effective radius and peak surface brightness are recovered to within 20 per cent for 98 and 94 per cent of the sources, respectively. Overall, we find that DECORAS provides a reliable source detection and characterization solution for future wide-field VLBI surveys.

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