Photometric redshift analysis in the Dark Energy Survey Science Verification data

By C. Sánchez, Matias Carrasco Kind, Huan Lin, R. Miquel, Filipe B. Abdalla, Adam Amara, M. Banerji, C. Bonnett, Robert J. Brunner, D. Capozzi and 68 more. 2014

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We present results from a study of the photometric redshift performance of the Dark Energy Survey (DES), using the early data from a Science Verification (SV) period of observations in late 2012 and early 2013 that provided science-quality images for almost 200 sq.~deg.~at the nominal depth of the survey. We assess the photometric redshift performance using about 15000 galaxies with spectroscopic redshifts available from other surveys. These galaxies are used, in different configurations, as a calibration sample, and photo-$z$'s are obtained and studied using most of the existing photo-$z$ codes. A weighting method in a multi-dimensional color-magnitude space is applied to the spectroscopic sample in order to evaluate the photo-$z$ performance with sets that mimic the full DES photometric sample, which is on average significantly deeper than the calibration sample due to the limited depth of spectroscopic surveys. Empirical photo-$z$ methods using, for instance, Artificial Neural Networks or Random Forests, yield the best performance in the tests, achieving core photo-$z$ resolutions $\sigma_{68} \sim 0.08$. Moreover, the results from most of the codes, including template fitting methods, comfortably meet the DES requirements on photo-$z$ performance, therefore, providing an excellent precedent for future DES data sets.

Subfields: Astronomy and Astrophysics, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7a8bf3885a01a2db5d41dbc95b71731cb5da5a4c

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Citations in the corpus

Star-galaxy Classification Using Deep Convolutional Neural Networks (2016, methodology): “…for astronomical use (Carrasco Kind & Brunner 2013); it has been tested for astronomical use cases, including photometric redshift estimation (Sánchez et al. 2014) and star-galaxy classification (Kim et al. 2015); and it uses parallelism to handle large data sets on distributed memory…”
The DES Science Verification weak lensing shear catalogues (2015, methodology): “For weak lensing we used these coadd images only for object detection, deblending, fluxes (for use in photometric redshift measurements, see Sánchez et al. 2014; Bonnett et al. 2015), and for the detailed informational flags which were important for determining a good set of galaxies to use for…”
Dark Energy Survey Year 1 Results: redshift distributions of the weak-lensing source galaxies (2017, methodology): “…2000; Bender et al. 2001; Collister & Lahav 2004; Feldmann et al. 2006; Ilbert et al. 2006; Hildebrandt et al. 2010; Carrasco Kind & Brunner 2013; Sánchez et al. 2014; Rau et al. 2015; Hoyle 2016; Sadeh, Abdalla & Lahav 2016; De Vicente, Sanchez & Sevilla- 1While there is Y- band data…”