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  Improving the reliability of photometric redshift with machine learning

Razim, O., Cavuoti, S., Brescia, M., Riccio, G., Salvato, M., & Longo, G. (2021). Improving the reliability of photometric redshift with machine learning. Monthly Notices of the Royal Astronomical Society, 507(4), 5034-5052. doi:10.1093/mnras/stab2334.

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Razim, Oleksandra, Author
Cavuoti, Stefano, Author
Brescia, Massimo, Author
Riccio, Giuseppe, Author
Salvato, Mara1, Author              
Longo, Giuseppe, Author
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1High Energy Astrophysics, MPI for Extraterrestrial Physics, Max Planck Society, ou_159890              

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 Abstract: In order to answer the open questions of modern cosmology and galaxy evolution theory, robust algorithms for calculating photometric redshifts (photo-z) for very large samples of galaxies are needed. Correct estimation of the various photo-z algorithms’ performance requires attention to both the performance metrics and the data used for the estimation. In this work, we use the supervised machine learning algorithm MLPQNA (Multi-Layer Perceptron with Quasi-Newton Algorithm) to calculate photometric redshifts for the galaxies in the COSMOS2015 catalogue and the unsupervised Self-Organizing Maps (SOM) to determine the reliability of the resulting estimates. We find that for zspec < 1.2, MLPQNA photo-z predictions are on the same level of quality as spectral energy distribution fitting photo-z. We show that the SOM successfully detects unreliable zspec that cause biases in the estimation of the photo-z algorithms’ performance. Additionally, we use SOM to select the objects with reliable photo-z predictions. Our cleaning procedures allow us to extract the subset of objects for which the quality of the final photo-z catalogues is improved by a factor of 2, compared to the overall statistics.

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Language(s): eng - English
 Dates: 2021-08-13
 Publication Status: Published online
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1093/mnras/stab2334
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Title: Monthly Notices of the Royal Astronomical Society
  Abbreviation : Mon. Not. Roy. Astron. Soc.
Source Genre: Journal
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Pages: - Volume / Issue: 507 (4) Sequence Number: - Start / End Page: 5034 - 5052 Identifier: ISSN: 0035-8711
ISSN: 1365-8711