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In pulsar astronomy, detecting effective pulsar signals among numerous pulsarcandidates is an important research topic. Starting from space X-ray pulsarsignals, the two-dimensional autocorrelation profile map (2D-APM) featuremodelling method, which utilizes epoch folding of the autocorrelation functionof X-ray signals and expands the time-domain information of the periodic axis,is proposed. A uniform setting criterion regarding the time resolution of theperiodic axis addresses pulsar signals without any prior information. Comparedwith the traditional profile, the model has a strong anti-noise ability, agreater abundance of information and consistent characteristics. The newfeature is simulated with double Gaussian components, and the characteristicdistribution of the model is revealed to be closely related to the distancebetween the double peaks of the profile. Next, a deep convolutional neuralnetwork (DCNN) is built, named Inception-ResNet. According to the order of thepeak separation and number of arriving photons, 30 data sets based on thePoisson process are simulated to construct the training set, and theobservation data of PSRs B0531+21, B0540-69 and B1509-58 from the Rossi X-rayTiming Explorer (RXTE) are selected to generate the test set. The number oftraining sets and the number of test sets are 30,000 and 5,400, respectively.After achieving convergence stability, more than 99$\%$ of the pulsar signalsare recognized, and more than 99$\%$ of the interference is successfullyrejected, which verifies the high degree of agreement between the network andthe feature model and the high potential of the proposed method in searchingfor pulsars.

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