Volume 611, March 2018
|Number of page(s)||11|
|Section||Numerical methods and codes|
|Published online||10 April 2018|
Deep learning approach for classifying, detecting and predicting photometric redshifts of quasars in the Sloan Digital Sky Survey stripe 82★
LUPM UMR 5299 CNRS/UM, Université de Montpellier,
Montpellier Cedex 05, France
2 CPPM, CNRS-IN2P3, Université Aix Marseille II, CC 907, 13288 Marseille Cedex 9, France
3 LIRMM UMR 5506 - team ICAR, Université de Montpellier, Campus St Priest, 34090 Montpellier, France
4 LSIS UMR 7296, CNRS, ENSAM, Université de Toulon et Aix-Marseille, Bâtiment Polytech, 13397 Marseille, France
Accepted: 3 November 2017
We have applied a convolutional neural network (CNN) to classify and detect quasars in the Sloan Digital Sky Survey Stripe 82 and also to predict the photometric redshifts of quasars. The network takes the variability of objects into account by converting light curves into images. The width of the images, noted w, corresponds to the five magnitudes ugriz and the height of the images, noted h, represents the date of the observation. The CNN provides good results since its precision is 0.988 for a recall of 0.90, compared to a precision of 0.985 for the same recall with a random forest classifier. Moreover 175 new quasar candidates are found with the CNN considering a fixed recall of 0.97. The combination of probabilities given by the CNN and the random forest makes good performance even better with a precision of 0.99 for a recall of 0.90. For the redshift predictions, the CNN presents excellent results which are higher than those obtained with a feature extraction step and different classifiers (a K-nearest-neighbors, a support vector machine, a random forest and a Gaussian process classifier). Indeed, the accuracy of the CNN within |Δz| < 0.1 can reach 78.09%, within |Δz| < 0.2 reaches 86.15%, within |Δz| < 0.3 reaches 91.2% and the value of root mean square (rms) is 0.359. The performance of the KNN decreases for the three |Δz| regions, since within the accuracy of |Δz| < 0.1, |Δz| < 0.2, and |Δz| < 0.3 is 73.72%, 82.46%, and 90.09% respectively, and the value of rms amounts to 0.395. So the CNN successfully reduces the dispersion and the catastrophic redshifts of quasars. This new method is very promising for the future of big databases such as the Large Synoptic Survey Telescope.
Key words: methods: data analysis / techniques: photometric / techniques: image processing / quasars: general / surveys
A table of the candidates is only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (188.8.131.52) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/611/A97
© ESO 2018
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