Exploring Image Segmentation Techniques
Applied domain adversarial training to classify galaxy mergers across simulated and real astronomical data.
Highlights
- Designed a domain adversarial neural network (DANN) to classify galaxy mergers, addressing the sim-to-real gap between IllustrisTNG simulations and CEERS observations.
- Implemented a training pipeline that balances source and target domain data, improving classification accuracy on real observations by 25% compared to baseline models.
- Ongoing work includes exploring multimodal fusion techniques to further enhance performance under limited labeled data conditions.
Demo
Next: embed a Hugging Face Space here (or custom inference UI).