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arXiv · 2103.01431

A Method for Pulsar Searching: Combining a Two-dimensional Autocorrelation Profile Map and a Deep Convolutional Neural Network

Longqi Wang·Jing Jin·Lu Liu·Yi Shen
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Abstract

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 5,400, 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.

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Longqi Wang, Jing Jin, Lu Liu, Yi Shen. 2021-03-02. A Method for Pulsar Searching: Combining a Two-dimensional Autocorrelation Profile Map and a Deep Convolutional Neural Network. https://doi.org/10.1088/1674-4527%2F21%2F7%2F171

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