A method for pulsar searching: combining a two-dimensional autocorrelation profile map and a deep convolutional neural network
A method for pulsar searching: combining a two-dimensional autocorrelation profile map and a deep convolutional neural network
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一种脉冲星搜索方法：二维自相关剖面图与深度卷积神经网络相结合
DOI：
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发表时间：
2021
影响因子：
1.8
通讯作者：
Yi Shen
中科院分区：
文献类型：
--
作者：
Longqi Wang;Jing Jin;Lu Liu;Yi Shen
在脉冲星天文学中，从众多的候选脉冲星中检测出有效的脉冲星信号是一个重要的研究课题。从空间X射线脉冲星信号出发，提出了利用X射线信号自相关函数历元折叠和扩展周期轴时域信息的二维自相关轮廓图（2D-APM）特征建模方法。关于周期轴的时间分辨率的统一设置标准在没有任何先验信息的情况下处理脉冲星信号。与传统剖面相比，该模型抗噪能力强，信息量更丰富，特征更一致。用双高斯分量对新特征进行了模拟，揭示了模型的特征分布与轮廓双峰之间的距离密切相关。接下来，构建一个深度卷积神经网络（DCNN），命名为Inception-ResNet。根据峰值分离的顺序和到达光子数，模拟30个基于泊松过程的数据集构建训练集，并选取Rossi X射线定时探测器（RXTE）的PSR B 0531 +21、B 0540 -69和B1509-58观测数据生成测试集。训练集和测试集的数量分别为30000和5400。在达到收敛稳定性后，对脉冲星信号的识别率超过99%，干扰的剔除率超过99%，验证了网络与特征模型的高度一致性以及所提方法在脉冲星搜索中的巨大潜力。
In pulsar astronomy, detecting effective pulsar signals among numerous pulsar candidates is an important research topic. Starting from space X-ray pulsar signals, the two-dimensional autocorrelation profile map (2D-APM) feature modelling method, which utilizes epoch folding of the autocorrelation function of X-ray signals and expands the time-domain information of the periodic axis, is proposed. A uniform setting criterion regarding the time resolution of the periodic axis addresses pulsar signals without any prior information. Compared with the traditional profile, the model has a strong anti-noise ability, a greater abundance of information and consistent characteristics. The new feature is simulated with double Gaussian components, and the characteristic distribution of the model is revealed to be closely related to the distance between the double peaks of the profile. Next, a deep convolutional neural network (DCNN) is built, named Inception-ResNet. According to the order of the peak separation and number of arriving photons, 30 data sets based on the Poisson process are simulated to construct the training set, and the observation data of PSRs B0531+21, B0540-69 and B1509-58 from the Rossi X-ray Timing Explorer (RXTE) are selected to generate the test set. The number of training sets and the number of test sets are 30 000 and 5400, respectively. After achieving convergence stability, more than 99% of the pulsar signals are recognized, and more than 99% of the interference is successfully rejected, which verifies the high degree of agreement between the network and the feature model and the high potential of the proposed method in searching for pulsars.
DOI：
10.1111/j.1365-2966.2012.22042.x
发表时间：
2012-09
影响因子：
4.8
作者：
S. Bates;S. Bates;M. Bailes;B. Barsdell;N. Bhat;M. Burgay;S. Burke-Spolaor;D. Champion;P. Coster;N. D'amico;A. Jameson;S. Johnston;M. Keith;M. Kramer;L. Levin;L. Levin;A. Lyne;S. Milia;S. Milia;C. Ng;C. Nietner;A. Possenti;B. Stappers;D. Thornton;W. Straten
通讯作者：
S. Bates;S. Bates;M. Bailes;B. Barsdell;N. Bhat;M. Burgay;S. Burke-Spolaor;D. Champion;P. Coster;N. D'amico;A. Jameson;S. Johnston;M. Keith;M. Kramer;L. Levin;L. Levin;A. Lyne;S. Milia;S. Milia;C. Ng;C. Nietner;A. Possenti;B. Stappers;D. Thornton;W. Straten
影响因子：
6.5
作者：
T. Coenen;J. V. Leeuwen;J. Hessels;B. Stappers;V. Kondratiev;A. Alexov;R. Breton;A. Bilous;S. Cooper;H. Falcke;H. Falcke;R. Fallows;V. Gajjar;J. Grießmeier;T. Hassall;A. Karastergiou;E. Keane;M. Kramer;M. Kuniyoshi;A. Noutsos;S. Osłowski;S. Osłowski;M. Pilia;M. Serylak;C. Schrijvers;C. Sobey;S. Veen;J. Verbiest;P. Weltevrede;S. Wijnholds;K. Zagkouris;A. V. Amesfoort;James M. Anderson;A. Asgekar;I. Avruch;M. Bell;M. Bentum;G. Bernardi;P. Best;A. Bonafede;F. Breitling;J. Broderick;M. Brüggen;H. Butcher;B. Ciardi;A. Corstanje;A. Deller;S. Duscha;J. Eislöffel;R. Fender;C. Ferrari;W. Frieswijk;M. Garrett;F. Gasperin;E. D. Geus;A. Gunst;J. Hamaker;G. Heald;M. Hoeft;A. Horst;E. Juette;G. Kuper;C. Law;C. Law;G. Mann;R. McFadden;D. McKay-Bukowski;D. McKay-Bukowski;J. McKean;H. Munk;E. Orrú;H. Paas;M. Pandey-Pommier;A. Polatidis;W. Reich;A. Renting;H. Röttgering;A. Rowlinson;A. Scaife;D. Schwarz;J. Sluman;O. Smirnov;J. Swinbank;M. Tagger;Y. Tang;C. Tasse;S. Thoudam;C. Toribio;R. Vermeulen;C. Vocks;R. V. Weeren;O. Wucknitz;P. Zarka;A. Zensus
通讯作者：
T. Coenen;J. V. Leeuwen;J. Hessels;B. Stappers;V. Kondratiev;A. Alexov;R. Breton;A. Bilous;S. Cooper;H. Falcke;H. Falcke;R. Fallows;V. Gajjar;J. Grießmeier;T. Hassall;A. Karastergiou;E. Keane;M. Kramer;M. Kuniyoshi;A. Noutsos;S. Osłowski;S. Osłowski;M. Pilia;M. Serylak;C. Schrijvers;C. Sobey;S. Veen;J. Verbiest;P. Weltevrede;S. Wijnholds;K. Zagkouris;A. V. Amesfoort;James M. Anderson;A. Asgekar;I. Avruch;M. Bell;M. Bentum;G. Bernardi;P. Best;A. Bonafede;F. Breitling;J. Broderick;M. Brüggen;H. Butcher;B. Ciardi;A. Corstanje;A. Deller;S. Duscha;J. Eislöffel;R. Fender;C. Ferrari;W. Frieswijk;M. Garrett;F. Gasperin;E. D. Geus;A. Gunst;J. Hamaker;G. Heald;M. Hoeft;A. Horst;E. Juette;G. Kuper;C. Law;C. Law;G. Mann;R. McFadden;D. McKay-Bukowski;D. McKay-Bukowski;J. McKean;H. Munk;E. Orrú;H. Paas;M. Pandey-Pommier;A. Polatidis;W. Reich;A. Renting;H. Röttgering;A. Rowlinson;A. Scaife;D. Schwarz;J. Sluman;O. Smirnov;J. Swinbank;M. Tagger;Y. Tang;C. Tasse;S. Thoudam;C. Toribio;R. Vermeulen;C. Vocks;R. V. Weeren;O. Wucknitz;P. Zarka;A. Zensus