VizieR Online Data Catalog: COSMOS2015 dataset machine learning photo-z (Razim+, 2021)

Abstract

We present here a catalogue of photometric redshifts obtained with a supervised Machine Learning algorithm called Multi Layer Perceptron with Quasi Newton Algorithm software (MLPQNA, Brescia et al., 2013ApJ...772..140B, 2014A&A...568A.126B, Cat. J/A+A/568/A126) for more than 200000 galaxies from the COSMOS2015 catalogue (Laigle et al., 2016ApJS..224...24L, Cat. J/ApJS/224/24). Following the limitations imposed by the training sample, the photo-z are reported for the sources with presumed true redshifts <1.2. ML photo-z are obtained using 10-band IR, visual and UV photometry. For the test sample of galaxies ML photo-z have std of residuals ~0.048 and percentage of catastrophic outliers ~1.64. In addition to this we provide reliability indicators for the photo-z obtained with Self-Organizing Maps. These indicators allow to detect anomalous spectral redshifts (in the train and test samples; the nature of these anomalous spec-z can be either physical (e.g. AGNs) or instrumental (e.g. misclassification of a spectral line)) and unreliable photo-z (in the whole dataset). Using these indicators it is possible to select highly reliable photo-z samples. The detailed description of the methodology for calculating and using the reliability indicators can be found in the paper. <P />The catalogue contains information for 214398 galaxies selected from the COSMOS2015 dataset (Laigle et al., 2016ApJS..224...24L, Cat. J/ApJS/224/24). The catalogue reports basic information about these galaxies according to the COSMOS2015: their sky coordinates (DEJ2000 and RAJ2000), their identifier within the COSMOS2015 (Seq) and SED fitting photo-z (photoZ_SED). Additionally, the catalogue contains ML photo-z (photoZ_ML), residual between ML and SED photo-z, a flag, reporting whether the given galaxy was included in the train, test or run datasets during the training of the ML model, and reliability metrics for ML photo-z, SED photo-z and spec-z. The in-cell outlier coefficients (photoZ<SUB>ML</SUB>outlCoeff, photoZ<SUB>SED</SUB>outlCoeff, specZ_outlCoeff) have the meaning of the number of sigmas by which the redshift of a given galaxy differs from the mean redshift of all galaxies belonging to the same SOM cell as this galaxy (see paper for the details on these indicators). Occupation of the cell (trainMapOccupation) reports how many galaxies from the train set belong to the cell of the given galaxy; the higher this number, the higher is the reliability of the photo-z prediction. For a highly reliable dataset it is recommended to discard galaxies with trainMapOccupation<5. <P />(1 data file).