拼接卷积神经网络用于脉冲星候选体选择
Concat Convolutional Neural Network for Pulsar Candidate Selection
AI总结：
针对脉冲星候选体选择难题，提出拼接卷积神经网络（CCNN），利用CNN提取特征并拼接，实现端到端识别，在FAST数据上仅漏掉4个真实脉冲星，优于现有模型。
摘要：
脉冲星搜索对于物理学和天体物理学领域的科学研究至关重要。随着射电望远镜的发展，候选体数量的爆炸式增长及其增长速度带来了若干挑战。因此，迫切需要开发一种自动、准确且高效的脉冲星候选体选择方法。为满足这一需求，本研究设计了一种拼接卷积神经网络（CCNN），用于识别从五百米口径球面射电望远镜（FAST）数据中收集的候选体。CCNN利用卷积神经网络（CNN）从诊断子图中提取“类脉冲星”模式，并通过拼接层将这些CNN特征结合起来。因此，CCNN是一种端到端的学习模型，无需任何中间标签，这使得CCNN适用于脉冲星候选体选择的在线学习流程。在FAST数据上的实验结果表明，CCNN在类似场景中优于现有的最先进模型。在总共326个候选体中，仅漏掉了4个真实脉冲星。
英文摘要：
Pulsar searching is essential for the scientific research in the field of physics and astrophysics. As the development of the radio telescope, the exploding volume and it growth speed of candidates growth have brought about several challenges. Therefore, there is an urgent demand for developing an automatic, accurate and efficient pulsar candidate selection method. To meet this need, this work designed a Concat Convolutional Neural Network (CCNN) to identify the candidates collected from the Five-hundred-meter Aperture Spherical Telescope (FAST) data. The CCNN extracts some "pulsar-like" patterns from the diagnostic subplots using Convolutional Neural Network (CNN) and combines these CNN features by a concatenate layer. Therefore, the CCNN is an end-to-end learning model without any need for any intermediate labels, which makes CCNN suitable for the online learning pipeline of pulsar candidate selection. Experimental results on FAST data show that the CCNN outperforms the available state-of-the-art models in similar scenario. It only misses 4 real pulsars out of 326 totally.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
