Photometric redshift estimation of SDSS galaxies

Machine Learning models for the extraction of Photometric Redshifts from 64*64 ugriz images from the SDSS survey as part of my internship, titled: "Machine Learning for photometric redshift estimation of LSST galaxies"

Any questions/comments are welcome.

Author

Anastasios Theodoropoulos

Supervisor

Prof. Simona Mei

Useful References

Pasquet, J., Bertin, E., Treyer, M., Arnouts, S., & Fouchez, D. (2019). Photometric redshifts from SDSS images using a convolutional neural network. Astronomy & Astrophysics, 621, A26.

Dey, B., Andrews, B. H., Newman, J. A., Mao, Y. Y., Rau, M. M., & Zhou, R. (2021). Photometric Redshifts from SDSS Images with an Interpretable Deep Capsule Network. arXiv preprint arXiv:2112.03939.

Salvato, M., Ilbert, O., & Hoyle, B. (2019). The many flavours of photometric redshifts. Nature Astronomy, 3(3), 212-222.

https://biprateep.de/encapZulate-1/ Useful website with code and link to the dataset used.