MFPIM: A Deep Learning Model Based on Multimodal Fusion Technology for Pulsar Identification
MFPIM: A Deep Learning Model Based on Multimodal Fusion Technology for Pulsar Identification
复制标题
DOI：
10.3847/1538-4357/acd9c8
复制
发表时间：
2023-08
期刊：
影响因子：
--
通讯作者：
Yi Liu;Jing Jin;Hongyang Zhao;Xujie He;Yanan Guo
中科院分区：
文献类型：
--
作者：
Yi Liu;Jing Jin;Hongyang Zhao;Xujie He;Yanan Guo
随着射电望远镜技术的发展，获得的脉冲星候选数据的数量和类型急剧增加。然而，很难准确地识别脉冲星候选者。因此，我们提出使用多模态融合技术，称为基于多模态融合的脉冲星识别模型（MFPIM），来构建深度学习模型，以提高脉冲星候选识别的效率和准确性。MFPIM将脉冲星候选者的每个诊断图视为一种模态，并使用多个卷积神经网络从诊断图中提取有效特征。通过特征融合，得到了不同模态在高维空间的共性，保证了模型能够充分利用诊断图之间的互补性，从而识别脉冲星候选，取得了比目前其他有监督学习算法更好的分类性能。此外，在模型中引入了通道注意机制，使模型能够学习不同通道特征的重要性，从而使模型更加关注输入数据中对分类更有意义的通道信息，在减小模型规模的同时更准确地提取脉冲星诊断图特征。在500米口径球面射电望远镜（FAST）数据集上进行了实验，结果表明，MFPIM能够有效地识别FAST数据集中的双星，识别准确率达到98%以上。为了进一步验证模型的鲁棒性，我们使用迁移学习将MFPIM应用于高时间分辨率宇宙数据集，测试准确率和F1得分达到99%以上。
With the development of radio telescope technology, the quantity and types of acquired pulsar candidate data have increased dramatically. However, it is difficult to accurately identify pulsar candidates. Therefore, we propose to use multimodal fusion technology, called the multimodal fusion-based pulsar identification model (MFPIM), to build a deep learning model to improve the efficiency and accuracy of pulsar candidate identification. MFPIM treats each diagnostic plot of pulsar candidates as a modality and uses multiple convolutional neural networks to extract effective features from the diagnostic plots. After fusing the features, the commonality of different modalities in high-dimensional space is obtained to ensure that the model can take full advantage of the complementarity between diagnostic plots and thus identify pulsar candidates, achieved better classification performance than other current supervised learning algorithms. In addition, a channel attention mechanism is used in the model to enable it to learn the importance of different channel features so that the model focuses more on the channel information in the input data that is more meaningful for classification, reducing the model size while extracting pulsar diagnostic map features more accurately. We conducted experiments on the Five-hundred-meter Aperture Spherical radio Telescope (FAST) data set, and the results show that MFPIM can effectively identify the pulsars in the FAST data set with an identification accuracy of over 98%. To further verify the robustness of the model, we applied the MFPIM to the High Time Resolution Universe data set using transfer learning, with the test accuracy and F1 score reaching over 99%.