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We apply a convolutional neural network (CNN) to classify and detect quasarsin the Sloan Digital Sky Survey Stripe 82 and also to predict the photometricredshifts of quasars. The network takes the variability of objects into accountby 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 itsprecision is 0.988 for a recall of 0.90, compared to a precision of 0.985 forthe same recall with a random forest classifier. Moreover 175 new quasarcandidates are found with the CNN considering a fixed recall of 0.97. Thecombination of probabilities given by the CNN and the random forest makes goodperformance even better with a precision of 0.99 for a recall of 0.90.  For the redshift predictions, the CNN presents excellent results which arehigher than those obtained with a feature extraction step and differentclassifiers (a K-nearest-neighbors, a support vector machine, a random forestand a gaussian process classifier). Indeed, the accuracy of the CNN within|\Delta z|<0.1 can reach 78.09%, within |\Delta z|<0.2 reaches 86.15%, within|\Delta z|<0.3 reaches 91.2% and the value of rms is 0.359. The performance ofthe KNN decreases for the three |\Delta z| regions, since within the accuracyof |\Delta z|<0.1, |\Delta z|<0.2 and |\Delta z|<0.3 is 73.72%, 82.46% and90.09% respectively, and the value of rms amounts to 0.395. So the CNNsuccessfully reduces the dispersion and the catastrophic redshifts of quasars.This new method is very promising for the future of big databases like theLarge Synoptic Survey Telescope.

Deep learning Approach for Classifying, Detecting and Predicting Photometric Redshifts of Quasars in the Sloan Digital Sky Survey Stripe 82