# Deep-Learning-Based Identification of Broad-Absorption Line Quasars
## 3 Method
### Deep Learning Model  
In Section 3.1, we applied various methods of dimension reduction for the extraction of features. Next, we used the simplified data as inputs for a deep learning classification model and formed this model to classify quasars. A convolutional neural network (CNN) is a very successful deep learning model for image processing and computer vision tasks. It automatically learns the features of images through convolution, pool, and full connection layers, allowing tasks such as image classification and object detection to be performed [35]. For the classification of quasars, spectral data can be used as a particular type of one-dimensional image, allowing a CNN to learn the features within a spectrum to distinguish BAL quasars from non-BAL quasars. In astronomy, a CNN can be used for various tasks such as galaxy classification, object detection, spectral classification, etc. [36; 37; 38]. For example, as part of a mission to classify galaxy images, a CNN can learn to distinguish different types of galaxies, such as elliptical and spiral galaxies, using images observed through a telescope [36]. The convolutions capture the texture and shape features of galaxies, allowing for high-precision classification. In object detection missions, a CNN can be used to detect targets such as planets, stars, and galaxies in celestial patrol data [39]. By sliding a convolution core over an image, a CNN can automatically locate astronomical objects. As part of spectral classification tasks, a CNN can learn the features of spectral data in different wavelength regions and perform tasks similar to image classification. For example, with quasar spectral data, a CNN can distinguish between broad- and non-broad-absorption lines [40].  
Therefore, the in-depth learning model of this study used a CNN as the basic architecture, the structure of which is illustrated in Figure 3. This model took one-dimensional spectral data as inputs and then easily extracted customer features using three convolution frames. Each frame was composed of a convolution layer, an activation function, and a pool layer. The convolution layer comprised a \(1\times 3\) convolution core that increased the number of channels from 64 to 128 and possibly to 256, allowing the model to explore in-depth representations of more complex features. The activation function used in the model was the rectified linear unit (ReLU) function, while the pooling layer comprised a maximum \(1\times 2\) pooling policy to compress the features (i.e., CARDS). After the feature extraction, the features were affected by a dropout layer that randomly eliminated 50% of the units, including their connections, to mitigate the risk of overshoot. The data were then propagated to the fully connected layer into which the model exported the results of the classification. Our model was trained for 15 epochs, which was sufficient to achieve a balance between underfitting and overfitting for this dataset, and we used the Adam optimizer because of its adaptive-learning rate properties. In addition, we applied a learning rate reduction strategy in which the learning rate was dynamically adjusted as training progressed.  
Figure 3: Overall architecture of the proposed deep learning model (Conv denotes convolution operation and BN denotes batch normalization).