Improving the reliability of photometric redshift with machine learning. Issue 4 (13th August 2021)

• Record Type: Journal Article
• Authors: Razim, Oleksandra; Cavuoti, Stefano; Brescia, Massimo; Riccio, Giuseppe; Salvato, Mara; Longo, Giuseppe
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 507:Issue 4(2021)
• Page Start: 5034
• Page End: 5052
• Publication Date: 2021-08-13
• Subjects: methods: data analysis -- techniques: spectroscopic -- surveys -- galaxies: distances and redshifts -- catalogues; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stab2334
• Languages: English
• ISSNs: 0035-8711

Abstract

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 z spec < 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 z spec 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.