Enhancing Pulsar Candidate Identification with Self-tuning Pseudolabeling Semisupervised Learning
In the field of astronomy, machine-learning technologies are becoming increasingly crucial for identifying radio pulsars. However, the process of acquiring labeled data, which is both time-consuming and potentially biased, poses a significant limitation to current methodologies. In response to these...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2024-01-01
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| Series: | The Astrophysical Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-4357/ad3e7f |