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Gschwend, J.; Rossel, A. C.; Ogando, R. L. C.; Neto, A. F.; Maia, M. A. G.; Costa, L. N. da; Lima, M.; Pellegrini, P.; Campisano, R.; Singulani, C.; Adean, C.; Benoist, C.; Aguena, M.; Kind, M. Carrasco; Davis, T. M.; De Vicente, J.; Hartley, W. G.; Hoyle, B.; Palmese, A.; Sadeh, I; Abbott, T. M. C.; Abdalla, F. B.; Allam, S.; Annis, J.; Asorey, J.; Brooks, D.; Calcino, J.; Carollo, D.; Castander, F. J.; D'Andrea, C. B.; Desai, S.; Evrard, A. E.; Fosalba, P.; Frieman, J.; Garcia-Bellido, J.; Glazebrook, K.; Gerdes, D. W.; Gruendl, R. A.; Gutierrez, G.; Hinton, S.; Hollowood, D. L.; Honscheid, K.; Hoormann, J. K.; James, D. J.; Kuehn, K.; Kuropatkin, N.; Lahav, O.; Lewis, G.; Lidman, C.; Lin, H.; Macaulay, E.; Marshall, J.; Melchior, P.; Miguel, R.; Moller, A.; Plazas, A. A.; Sanchez, E.; Santiago, B.; Scarpine, V; Schindler, Z. H.; Sevilla-Noarbe, I; Smith, M.; Sobreira, F.; Sommer, N. E.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; Tucker, B. E.; Tucker, D. L.; Uddin, S.; Walker, A. R. (2018): DES science portal: Computing photometric redshifts. In: Astronomy and Computing, Vol. 25: pp. 58-80
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Abstract

A significant challenge facing photometric surveys for cosmological purposes is the need to produce reliable redshift estimates. The estimation of photometric redshifts (photo-zs) has been consolidated as the standard strategy to bypass the high production costs and incompleteness of spectroscopic redshift samples. Training-based photo-z methods require the preparation of a high-quality list of spectroscopic redshifts, which needs to be constantly updated. The photo-z training, validation, and estimation must be performed in a consistent and reproducible way in order to accomplish the scientific requirements. To meet this purpose, we developed an integrated web-based data interface that not only provides the framework to carry out the above steps in a systematic way, enabling the ease testing and comparison of different algorithms, but also addresses the processing requirements by parallelizing the calculation in a transparent way for the user. This framework called the Science Portal (hereafter Portal) was developed in the context the Dark Energy Survey (DES) to facilitate scientific analysis. In this paper, we show how the Portal can provide a reliable environment to access vast datasets, provide validation algorithms and metrics, even in the case of multiple photo-zs methods. It is possible to maintain the provenance between the steps of a chain of workflows while ensuring reproducibility of the results. We illustrate how the Portal can be used to provide photo-z estimates using the DES first year (Y1A1) data. While the DES collaboration is still developing techniques to obtain more precise photo-zs, having a structured framework like the one presented here is critical for the systematic vetting of DES algorithmic improvements and the consistent production of photo-zs in future DES releases.

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