使用多输入卷积神经网络的脉冲星候选体筛选
Pulsar Candidate Sifting Using Multi-input Convolution Neural Networks
AI总结：
针对脉冲星候选体筛选中类别高度不平衡导致机器学习困难的问题，提出多输入卷积神经网络（MICNN），以四个诊断图为输入，结合图像增强与三阶段训练策略，在HTRU和GBNCC数据上实现高召回率与精确率。
摘要：
脉冲星候选体筛选是发现新脉冲星的关键过程，其目标是从全天巡天数据（如高时间分辨率宇宙（HTRU）、绿岸北方天区（GBNCC）、五百米口径球面射电望远镜（FAST）等）中搜寻最有希望的脉冲星候选体。近年来，机器学习（ML）成为脉冲星候选体筛选研究的热点。然而，机器学习在脉冲星候选体筛选中面临的一个典型挑战是，由于脉冲星与非脉冲星观测数量之间的高度类别不平衡，导致学习困难。为此，本研究提出了一种新颖的候选体筛选框架，名为多输入卷积神经网络（MICNN）。MICNN是一种深度学习架构，以脉冲星候选体的四个诊断图作为输入。为了在高度类别不平衡的数据集上训练我们的MICNN，我们提出了一种新颖的图像增强技术以及三阶段训练策略。在HTRU和GBNCC观测数据上的实验表明，所提出的技术具有有效性和鲁棒性。在HTRU上的实验中，即使在高类别不平衡的测试数据集上，我们的MICNN模型也实现了0.962的召回率和0.967的精确率。
英文摘要：
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.
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。
