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Pulsar-VLM
jcchtt/Pulsar-VLM
Pulsar-VLM is a machine learning model from jcchtt. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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-…
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At a glance
- Model type
- multi_modality
- Access
- Public
- Created
- Oct 27, 2025
- Updated
- Nov 10, 2025
- SHA
- de191baa
Model card
From the Hugging Face model README
1. Introduction
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.
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.
2. Evaluation Dataset
Below are the pulsar candidate datasets used in this work:
-
CRAFTS Dataset: Hugging Face Repository
Provided by Dr. Pei Wang, Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University. Email: [email protected] -
GPPS Dataset: Official Website
-
GC FANS Dataset: Official Website
Diagnostic plots generated with PRESTO, from which we identified 91 pulsars. Our processed dataset is available at: Hugging Face Repository -
FAST Dataset: GitHub Repository
-
GBT-350 Dataset: Official Website
-
GBNCC Dataset: Official Website
-
SGAN Dataset: GitHub Repository
-
CHIRSS Dataset: Official Website
-
LOTAAS Dataset: Official Website
3. Evaluation Results
| 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% |
4. Usage
- Type
- multi_modality
- Created
- Oct 27, 2025
- Updated
- Nov 10, 2025