Deep Transfer Learning for Star Cluster Classification: I. Application to the PHANGS-HST Survey.

@article{Wei2019DeepTL,
  title={Deep Transfer Learning for Star Cluster Classification: I. Application to the PHANGS-HST Survey.},
  author={W. Wei and E. Huerta and B. Whitmore and Janice C. Lee and S. Hannon and R. Chandar and D. Dale and K. Larson and D. Thilker and L. {\'U}beda and M. Boquien and M. Chevance and J. Kruijssen and A. Schruba and G. Blanc and E. Congiu},
  journal={arXiv: Astrophysics of Galaxies},
  year={2019}
}
We present the results of a proof-of-concept experiment which demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in HST UV-optical imaging of nearby spiral galaxies in the PHANGS-HST survey. Given the relatively small and unbalanced nature of existing, human-labelled star cluster datasets, we transfer the knowledge of neural network models for real-object recognition to classify star clusters candidates into four… Expand

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