| # 1. Introduction |
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| We propose **Pulsar-VLM**, a visual reasoning model for pulsar candidate identification, built on pretrained multimodal large language models (MLLMs). The input to Pulsar-VLM includes diagnostic subplots (i.e., frequency-phase plots and DM curves) along with task instructions. By integrating the visual features of the diagnostic subplots with semantic textual descriptions, Pulsar-VLM is able to perform geometric morphological reasoning on the diagnostic plots in a manner analogous to how astronomers interpret them. |
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| **Pulsar-VLM maintains high performance across diagnostic plots from commonly used radio telescopes, despite morphological differences, and thus provides substantial benefits to the pulsar research community.** |
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| # 2. Evaluation Dataset |
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| Below are the pulsar candidate datasets used in this work: |
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| - **CRAFTS Dataset**: [Hugging Face Repository](https://huggingface.co/datasets/jcchtt/CRAFTS_Dataset) |
| Provided by Dr. Pei Wang, Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University. Email: [wangpei@nao.cas.cn](mailto:wangpei@nao.cas.cn) |
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| - **GPPS Dataset**: [Official Website](http://zmtt.bao.ac.cn/GPPS/) |
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| - **GC FANS Dataset**: [Official Website](https://www3.mpifr-bonn.mpg.de/staff/pfreire/GCpsr.html) |
| Diagnostic plots generated with PRESTO, from which we identified 91 pulsars. Our processed dataset is available at: [Hugging Face Repository](https://huggingface.co/datasets/jcchtt/GC_FANS_Dataset) |
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| - **FAST Dataset**: [GitHub Repository](https://github.com/dzuwhf/FAST_label_data) |
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| - **GBT-350 Dataset**: [Official Website](http://astro.phys.wvu.edu/GBTdrift350/) |
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| - **GBNCC Dataset**: [Official Website](http://astro.phys.wvu.edu/GBNCC/) |
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| - **SGAN Dataset**: [GitHub Repository](https://github.com/vishnubk/sgan) |
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| - **CHIRSS Dataset**: [Official Website](https://www.jb.man.ac.uk/research/pulsars/GHRSS/) |
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| - **LOTAAS Dataset**: [Official Website](https://www.astron.nl/lotaas/index.php?sort=0&order=0) |
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| # 3. Evaluation Results |
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| | Dataset | FN | Recall | |
| |-----------------|-----|--------| |
| | CRAFTS Dataset | 3 | 98.8% | |
| | GC FANS Dataset | 1 | 98.9% | |
| | GPPS Dataset | 3 | 99.8% | |
| | GBT-350 Dataset | 0 | 100% | |
| | GBNCC Dataset | 10 | 94.2% | |
| | LOTAAS Dataset | 0 | 100.0% | |
| | CHIRSS Dataset | 0 | 100.0% | |
| | FAST Dataset | 1 | 99.7% | |
| | SGAN Dataset | 577 | 94.5% | |
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| # 4. Usage |
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