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Table 3. Results of GAN-based generated images on the HTRU-Medlat dataset
Model & Dataset F1-score Recall Precision
CNN
Subints (IDS) 95.6% 94.8% 96.3%
Subints (BDS) 96.9% 94.0% 99.9%
Subbands (IDS) 95.8% 95.4% 96.2%
Subbands (BDS) 97.3% 94.8% 99.9%
ResNet
Subints (IDS) 97.3% 96.4% 98.3%
Subints (BDS) 98.2% 98.0% 98.4%
Subbands (IDS) 97.5% 95.7% 99.3%
Subbands (BDS) 98.3% 97.4% 99.3%

Table 4. Comparison of the effects of different pulsar candidate identification methods
Model Literature Disadvantage/Advantage
Eatough RP, Molkenthin N, Kramer M, Noutsos A, Keith M, et al. Selection
of radio pulsar candidates using artificial neural networks. Monthly
Traditional Time-consuming
Notices of the Royal Astronomical Society 2010;407:2443–50.
[DOI: 10.1111/j.1365-2966.2010.17082.x]
Bates S, Bailes M, Barsdell B, Bhat N, Burgay M, et al. The high time resolution
universe pulsar survey—VI. An artificial neural network and timing of 75
pulsars. Monthly Notices of the Royal Astronomical Society 2012;427:1052–65.
ANN/CNN [DOI: 10.1111/j.1365-2966.2012.22042.x] Sample imbalance,
Morello V, Barr E, Bailes M, Flynn C, Keane E, et al. SPINN: a straightforward poor classification
machine learning solution to the pulsar candidate selection problem. Monthly
Notices of the Royal Astronomical Society 2014;443:1651–62. [DOI: 10.1093/
mnras/stu1188]
Zhu W, Berndsen A, Madsen E, Tan M, Stairs I, et al. Searching for pulsars
using image pattern recognition. The Astrophysical Journal 2014;781:117.
[DOI: 10.1088/0004-637x/781/2/117]
Lyon RJ, Stappers B, Cooper S, Brooke JM, Knowles JD. Fifty years of pulsar
candidate selection: from simple filters to a new principled real-time
classification approach. Monthly Notices of the Royal
Astronomical Society 2016;459:1104–23. [DOI: 10.1093/mnras/stw656]
Guo P, Duan F, Wang P, Yao Y, Yin Q, et al. Pulsar candidate classification
GAN using generative adversary networks. Monthly Notices of the Royal Pattern collapse
Astronomical Society 2019;490:5424–39. [DOI: 10.1093/mnras/stz2975]
Alleviates sample imbalance problem,
The proposed model
improves the accuracy of recognition

and 97.5% in the ints and bands scenarios respectively, which indicates that the ResNet method is able to fit the
data better than the CNN method , and can extract features with more classification ability on a smaller dataset.
In addition, when using the BDS method, both models incorporate the simulated samples generated by the
generative adversarial network. It can be seen that for the ints and bands images, the CNN method improves
the F1 value by 1.3% and 1.5% on the BDS scenarios compared to the IDS scenarios, and the precision metric
improves by 3.6% and 3.7% compared to the IDS scenario. In addition, it can be seen that for the ints and
bands images, the ResNet method improves Recall by 1.6% and 1.7% for the BDS scenario compared to the
IDS scenario, andthe F1 valuesimprove by0.9% and0.8% compared totheIDS scenario. These results indicate
that the data expanded by the simulated samples generated by the generative adversarial network are of higher
quality and can provide richer recognition features, which causes the model’s recognition to be more accurate
to a certain extent.

Different pulsar candidate identification methods are contrasted respectively, as shown in Table 4. For the
traditional methods, manual experts review the pulsar candidates slowly, and thus, the models are evidently
time-consuming. Neural network-based methods have better identification results but suffer from the sample
imbalance problem. The traditional GAN model alleviates the sample imbalance problem to a certain extent
but suffers from the pattern collapse problem in the process of generating positive samples. Compared with
the previous methods, the proposed method alleviates the sample imbalance and pattern collapse problems,
and has a faster identification speed and higher identification accuracy.