Improving machine learning-derived photometric redshifts and physical property estimates using unlabelled observations Article Swipe
A. Humphrey
,
P. A. C. Cunha
,
Ana Paulino-Afonso
,
Stergios Amarantidis
,
Rodrigo Carvajal
,
J. M. Gomes
,
I. Matute
,
P. Papaderos
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/stac3596
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/stac3596
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 unlabelled data. We test the pseudo-labelling method on the problems of estimating redshift, stellar mass, and star formation rate, using COSMOS2015 broad band photometry and one of several publicly available machine learning algorithms, and we obtain significant improvements compared to purely supervised learning. We find that the gradient-boosting tree methods CatBoost, XGBoost, and LightGBM benefit the most, with reductions of up to ∼15 per cent in metrics of absolute error. We also find similar improvements in the photometric redshift catastrophic outlier fraction. We argue that the pseudo-labelling technique will be useful for the estimation of redshift and physical properties of galaxies in upcoming large imaging surveys such as Euclid and LSST, which will provide photometric data for billions of sources.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/mnras/stac3596
- OA Status
- green
- Cited By
- 11
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312091956
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4312091956Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/mnras/stac3596Digital Object Identifier
- Title
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Improving machine learning-derived photometric redshifts and physical property estimates using unlabelled observationsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-12-12Full publication date if available
- Authors
-
A. Humphrey, P. A. C. Cunha, Ana Paulino-Afonso, Stergios Amarantidis, Rodrigo Carvajal, J. M. Gomes, I. Matute, P. PapaderosList of authors in order
- Landing page
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https://doi.org/10.1093/mnras/stac3596Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
-
https://hal.science/hal-05194833Direct OA link when available
- Concepts
-
Photometric redshift, Computer science, Artificial intelligence, Photometry (optics), Machine learning, Boosting (machine learning), Outlier, Random forest, Redshift, Galaxy, Supervised learning, Astrophysics, Artificial neural network, Stars, Physics, Computer visionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 5, 2022: 1Per-year citation counts (last 5 years)
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50Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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