# Deep-Learning-Based Identification of Broad-Absorption Line Quasars
## 5 Conclusions  
This study examined the effectiveness of deep learning methods in distinguishing BAL quasars from non-BAL quasars. The impact of the different dimension reduction techniques on the classification of astronomical data was studied, and their respective performances were compared by reducing the size of the spectral data and applying a CNN classification. The PCA, t-SNE, LLE, and ISOMAP dimensionality reduction methods were assessed globally. The experimental results showed significant differences between these methods in capturing the features and patterns of the underlying data. PCA is a widely used linear reduction technique that effectively reduces data size while preserving critical variances, making it ideal for providing the input features needed for subsequent deep learning models. LLE and ISOMAP focus on capturing local relationships and global variety structures, respectively, and they show significant performances based on specific data features and patterns. Although t-SNE may be less effective for classification tasks, it has proven itself valuable in visualizing data distribution and revealing intrinsic structures in larger spaces. High-dimensional data often contain large amounts of redundant information and noise, which poses enormous challenges in computing and generalizing compared to traditional classification models. As a linear dimension reduction method, PCA effectively reduces data size by preserving key overall features, reducing model complexity, and improving drive efficiency. However, principal component analysis is essentially linear and cannot fully capture the potentially non-linear models of spectral data. CNNs have powerful feature extraction capabilities that allow for automatically learning local and global features through their hierarchical structures. This study combined the dimension reduction capability of PCA with a CNN's ability to learn features to obtain complementary benefits. PCA extracted key feature components from spectral data and efficiently filtered redundant information. The CNN also extracted deep feature models from the PCA-reduced data, improving the model's classification accuracy. The experimental results showed that the combined method not only improved the classification performance, but it also significantly reduced the computing volume and was particularly suitable for processing large-scale spectral datasets. Spectral signatures of wide-absorption line (BAL) quasars are often complex and varied, and conventional classification methods have difficulty in effectively capturing the subtle features of larger data. The method proposed here optimizes the classification task according to the following aspects: the highly adaptive selection of features, as this method used the main component analysis to select the first 100 main components, which ensured that the main physical information of the spectrum, such as the absorption characteristics of \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Our Best Model (\%)**} & \multicolumn{2}{c}{**[PERSON]’s Model (\%)**} \\ \hline \multirow{2}{*}{Precision} & Accuracy & 99.1 & 97.3 \\  & BAL quasars & 99.5 & 96.2 \\  & non-BAL quasars & 98.8 & 97.6 \\ \hline \multirow{2}{*}{Recall} & BAL quasars & 98.8 & 97.7 \\  & non-BAL quasars & 99.5 & 96.2 \\ \hline \multirow{2}{*}{F1 Score} & BAL quasars & 99.1 & 96.9 \\  & non-BAL quasars & 99.1 & 96.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with [PERSON]’s model. C IV and Si IV, were preserved during size reduction; and efficient model architecture, as the CNN used a one-dimensional convolution core to process the spectral data in combination with a pool layer and activation functions, which improved the model's ability to capture local features while avoiding overfitting. This study offers new approaches and perspectives for the classification of astronomical data, providing astronomers with tools to select the size reduction algorithms and deep learning frameworks best suited to their specific needs.