论文标题
使用多输入卷积神经网络的脉冲星候选人筛选
Pulsar Candidate Sifting Using Multi-input Convolution Neural Networks
论文作者
论文摘要
Pulsar候选筛分是发现新脉冲星的重要过程。它的目的是从一项全套调查中寻找最有前途的PULSAR候选人，例如高空分辨率宇宙（HTRU），绿色银行北部天体帽（GBNCC），五毫升的尖端球形射程望远镜（快速）等。最近，机器学习（ML）是Pulsar cantication cantication cantications contectation。但是，ML在脉冲星候选筛查中的一个典型挑战来自于脉冲星和非脉搏的观察数之间的高度阶级失情引起的学习困难。因此，这项工作提出了一个新的候选筛选框架，称为多输入卷积神经网络（MICNN）。 MICNN是深度学习的建筑，其中有四个诊断性的脉冲星候选者作为其输入。为了在高度的级别不平衡数据集中训练我们的MICNN，提出了一种新型的图像增强技术以及三阶段的培训策略。对HTRU和GBNCC观察结果的实验显示了这些提出的技术的有效性和鲁棒性。在HTRU的实验中，我们的MICNN模型的召回率为0.962，即使在高度平衡的测试数据集中，精度率也为0.967。
Pulsar candidate sifting is an essential process for discovering new pulsars. It aims to search for the most promising pulsar candidates from an all-sky survey, such as High Time Resolution Universe (HTRU), Green Bank Northern Celestial Cap (GBNCC), Five-hundred-meter Aperture Spherical radio Telescope (FAST), etc. Recently, machine learning (ML) is a hot topic in pulsar candidate sifting investigations. However, one typical challenge in ML for pulsar candidate sifting comes from the learning difficulty arising from the highly class-imbalance between the observation numbers of pulsars and non-pulsars. Therefore, this work proposes a novel framework for candidate sifting, named multi-input convolutional neural networks (MICNN). The MICNN is an architecture of deep learning with four diagnostic plots of a pulsar candidate as its inputs. To train our MICNN in a highly class-imbalanced dataset, a novel image augment technique, as well as a three-stage training strategy, is proposed. Experiments on observations from HTRU and GBNCC show the effectiveness and robustness of these proposed techniques. In the experiments on HTRU, our MICNN model achieves a recall of 0.962 and a precision rate of 0.967 even in a highly class-imbalanced test dataset.
