To understand the cosmic accretion history of supermassive black holes, separating the radiation from active galactic nuclei (AGNs) and star-forming galaxies (SFGs) is critical. However, a reliable solution on photometrically recognising AGNs still remains unsolved. In this work, we present a novel AGN recognition method based on Deep Neural Network (Neural Net; NN). The main goals of this work are (i) to test if the AGN recognition problem in the North Ecliptic Pole Wide (NEPW) field could be solved by NN; (ii) to shows that NN exhibits an improvement in the performance compared with the traditional, standard spectral energy distribution (SED) fitting method in our testing samples; and (iii) to publicly release a reliable AGN/SFG catalogue to the astronomical community using the best available NEPW data, and propose a better method that helps future researchers plan an advanced NEPW database. Finally, according to our experimental result, the NN recognition accuracy is around 80.29% 85.15%, with AGN completeness around 85.42% 88.53% and SFG completeness around 81.17% 85.09%.

中文翻译：

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基于深度神经网络的活动星系核识别模型

要了解超大质量黑洞的宇宙吸积历史，将辐射与活动星系核 (AGN) 和恒星形成星系 (SFG) 分开至关重要。然而，关于光度识别 AGN 的可靠解决方案仍未解决。在这项工作中，我们提出了一种基于深度神经网络（Neural Net；NN）的新型 AGN 识别方法。这项工作的主要目标是 (i) 测试 NN 是否可以解决北黄极宽 (NEPW) 领域的 AGN 识别问题；(ii) 表明在我们的测试样本中，与传统的标准光谱能量分布 (SED) 拟合方法相比，NN 在性能上有所改进；(iii) 使用可用的最佳 NEPW 数据向天文学界公开发布可靠的 AGN/SFG 目录，并提出一种更好的方法，帮助未来的研究人员规划先进的 NEPW 数据库。最后，根据我们的实验结果，NN 识别准确率在 80.29% 85.15% 左右，AGN 完整性在 85.42% 88.53% 左右，SFG 完整性在 81.17% 85.09% 左右。