论文标题
Concat卷积神经网络用于脉冲星候选
Concat Convolutional Neural Network for Pulsar Candidate Selection
论文作者
论文摘要
Pulsar搜索对于物理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，候选人增长的爆炸量和IT增长速度带来了一些挑战。因此，迫切需要开发一种自动，准确，有效的PULSAR候选方法。为了满足这一需求，这项工作设计了Concat卷积神经网络（CCNN），以确定从五百米的光圈球形望远镜（快速）数据中收集的候选者。 CCNN使用卷积神经网络（CNN）从诊断子图中提取了一些“脉冲星样”模式，并将这些CNN特征与连接层结合在一起。因此，CCNN是一个端到端的学习模型，无需任何中间标签，这使得CCNN适合于在线学习pulsar候选者选择。快速数据的实验结果表明，在类似情况下，CCNN优于可用的最新模型。它完全错过了326个中的4个真正的脉冲星。
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.
