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@ARTICLE{Gschwend:411437,
author = {Gschwend, J. and Rossel, A. C. and Ogando, R. L. C. and
Neto, A. F. and Maia, M. A. G. and da Costa, L. N. and Lima,
M. and Pellegrini, P. and Campisano, R. and Singulani, C.
and Adean, C. and Benoist, C. and Aguena, M. and Carrasco
Kind, M. and Davis, T. M. and de Vicente, J. and Hartley, W.
G. and Hoyle, B. and Palmese, A. and Sadeh, I. and Abbott,
T. M. C. and Abdalla, F. B. and Allam, S. and Annis, J. and
Asorey, J. and Brooks, D. and Calcino, J. and Carollo, D.
and Castander, F. J. and D’Andrea, C. B. and Desai, S. and
Evrard, A. E. and Fosalba, P. and Frieman, J. and
García-Bellido, J. and Glazebrook, K. and Gerdes, D. W. and
Gruendl, R. A. and Gutierrez, G. and Hinton, S. and
Hollowood, D. L. and Honscheid, K. and Hoormann, J. K. and
James, D. J. and Kuehn, K. and Kuropatkin, N. and Lahav, O.
and Lewis, G. and Lidman, C. and Lin, H. and Macaulay, E.
and Marshall, J. and Melchior, P. and Miquel, R. and
Möller, A. and Plazas, A. A. and Sanchez, E. and Santiago,
B. and Scarpine, V. and Schindler, R. H. and Sevilla-Noarbe,
I. and Smith, M. and Sobreira, F. and Sommer, N. E. and
Suchyta, E. and Swanson, M. E. C. and Tarle, G. and Tucker,
B. E. and Tucker, D. L. and Uddin, S. and Walker, A. R.},
title = {{DES} science portal: {C}omputing photometric redshifts},
journal = {Astronomy and computing},
volume = {25},
issn = {2213-1337},
address = {Amsterdam [u.a.]},
publisher = {Elsevier},
reportid = {PUBDB-2018-03718},
pages = {58-80},
year = {2018},
note = {© Elsevier B.V.},
abstract = {A significant challenge facing photometric surveys for
cosmological purposes is the need to produce reliable
redshift estimates. The estimation of photometric redshifts
(photo-s) has been consolidated as the standard strategy to
bypass the high production costs and incompleteness of
spectroscopic redshift samples. Training-based photo-
methods require the preparation of a high-quality list of
spectroscopic redshifts, which needs to be constantly
updated. The photo- 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-s 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-
estimates using the DES first year (Y1A1) data. While the
DES collaboration is still developing techniques to obtain
more precise photo-s, 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.},
cin = {ZEU-EXP/AT},
ddc = {520},
cid = {$I:(DE-H253)ZEU-EXP_AT-20120731$},
pnm = {613 - Matter and Radiation from the Universe (POF3-613)},
pid = {G:(DE-HGF)POF3-613},
experiment = {EXP:(DE-H253)CTA-20150101},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000451390700007},
doi = {10.1016/j.ascom.2018.08.008},
url = {https://bib-pubdb1.desy.de/record/411437},
}