Rivera, JD;
Moraes, B;
Merson, AI;
Jouvel, S;
Abdalla, FB;
Abdalla, MCB;
(2018)
Degradation analysis in the estimation of photometric redshifts from non-representative training sets.
Monthly Notices of the Royal Astronomical Society (MNRAS)
, 477
(4)
pp. 4330-4347.
10.1093/mnras/sty880.
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Abstract
We perform an analysis of photometric redshifts estimated by using a non-representative training sets in magnitude space. We use the ANNz2 and GPz algorithms to estimate the photometric redshift both in simulations and in real data from the Sloan Digital Sky Survey (DR12). We show that for the representative case, the results obtained by using both algorithms have the same quality, using either magnitudes or colours as input. In order to reduce the errors when estimating the redshifts with a non-representative training set, we perform the training in colour space. We estimate the quality of our results by using a mock catalogue which is split samples cuts in the r band between 19.4 < r < 20.8. We obtain slightly better results with GPz on single point z-phot estimates in the complete training set case, however the photometric redshifts estimated with ANNz2 algorithm allows us to obtain mildly better results in deeper r-band cuts when estimating the full redshift distribution of the sample in the incomplete training set case. By using a cumulative distribution function and a Monte Carlo process, we manage to define a photometric estimator which fits well the spectroscopic distribution of galaxies in the mock testing set, but with a larger scatter. To complete this work, we perform an analysis of the impact on the detection of clusters via density of galaxies in a field by using the photometric redshifts obtained with a non-representative training set.
| Type: | Article |
|---|---|
| Title: | Degradation analysis in the estimation of photometric redshifts from non-representative training sets |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/mnras/sty880 |
| Publisher version: | https://doi.org/10.1093/mnras/sty880 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Physical Sciences, Astronomy & Astrophysics, methods: data analysis, galaxies: distances and redshifts, GAUSSIAN PROCESS REGRESSION, MASS ASSEMBLY GAMA, DIGITAL SKY SURVEY, BARYON ACOUSTIC-OSCILLATIONS, ARTIFICIAL NEURAL-NETWORKS, OPTICAL CLUSTER SURVEY, LUMINOUS RED GALAXIES, DARK ENERGY, COSMOLOGICAL PARAMETERS, SDSS |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10057280 |
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