Photometric redshift estimation using ExtraTreesRegressor: Galaxies and quasars from low to very high redshifts
Author:
Publisher
Springer Science and Business Media LLC
Subject
Space and Planetary Science,Astronomy and Astrophysics
Link
http://link.springer.com/content/pdf/10.1007/s10509-020-03758-w.pdf
Reference19 articles.
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2. Carliles, S., et al.: Random forests for photometric redshifts. Astrophys. J. 712, 511–515 (2010)
3. Collister, A.A., Lahav, O.: ANNz: estimating photometric redshifts using artificial neural networks. Publ. Astron. Soc. Pac. 116, 345–351 (2004)
4. D’Isanto, A., et al.: Return of the features: efficient feature selection and interpretation for photometric redshifts. Astron. Astrophys. 616, A97 (2018). https://doi.org/10.1051/0004-6361/201833103
5. D’Isanto, A., Polsterer, K.L.: Photometric redshift estimation via deep learning. Astron. Astrophys. 609, A111 (2018). https://doi.org/10.1051/0004-6361/201731326
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