Improving machine learning-derived photometric redshifts and physical property estimates using unlabelled observations

@article{Humphrey2022ImprovingML,
  title={Improving machine learning-derived photometric redshifts and physical property estimates using unlabelled observations},
  author={Andrew Humphrey and P. A. C. Cunha and Ana Paulino-Afonso and Stergios Amarantidis and R. Carvajal and Jean Michel Gomes and Israel Matute and Polychronis Papaderos},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2022}
}
In the era of huge astronomical surveys, machine learning offers promising solutions for the efficient estimation of galaxy properties. The traditional, ‘supervised’ paradigm for the application of machine learning involves training a model on labelled data, and using this model to predict the labels of previously unlabelled data. The semi-supervised ‘pseudo-labelling’ technique offers an alternative paradigm, allowing the model training algorithm to learn from both labelled data and as-yet… 
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