We apply 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
|\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 of
the KNN decreases for the three |\Delta z| regions, since within the accuracy
of |\Delta z|<0.1, |\Delta z|<0.2 and |\Delta z|<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 like the
Large Synoptic Survey Telescope.

本研究使用卷积神经网络（CNN）对斯隆数字天空调查中的类星体进行分类和检测，以及预测类星体的光度红移，并结合随机森林分类器提高准确性，相较于其他分类器表现显著提高，在未来的大数据库例如大型巡天望远镜中具有很好的应用前景。

用于分类、检测和预测斯隆数字天区 Stripe 82 类星体光度红移的深度学习方法

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

We present an automatic classification method for astronomical catalogs with
missing data. We use Bayesian networks, a probabilistic graphical model, that
allows us to perform inference to pre- dict missing values given observed data
and dependency relationships between variables. To learn a Bayesian network
from incomplete data, we use an iterative algorithm that utilises sampling
methods and expectation maximization to estimate the distributions and
probabilistic dependencies of variables from data with missing values. To test
our model we use three catalogs with missing data (SAGE, 2MASS and UBVI) and
one complete catalog (MACHO). We examine how classification accuracy changes
when information from missing data catalogs is included, how our method
compares to traditional missing data approaches and at what computational cost.
Integrating these catalogs with missing data we find that classification of
variable objects improves by few percent and by 15% for quasar detection while
keeping the computational cost the same.

本文提出了一种利用贝叶斯网络进行天文目录分类的自动分类方法，利用基于采样的期望最大化算法来学习贝叶斯网络，并在 SAGE、2MASS 和 UBVI 等缺失数据的目录和一个完整的目录上测试了该方法，发现该方法在减少计算成本的同时提高了变量物体分类准确性。