利用卷积神经网络搜索暗能量光谱仪模拟光谱中的莱曼极限系统
摘要
<p indent="0mm">Studying Lyman limit systems (LLS) is crucial for a deeper understanding of the large-scale structure of the universe, the evolutionary history of galaxies, and the distribution of gas within galaxy clusters. Although LLS absorption features are distinctive, current research is largely constrained by these characteristics. Additionally, traditional methods are predominantly employed, with a primary focus on the identification and analysis of small sample sets with column densities ranging from <sc>10<sup>19</sup> cm<sup>−2</sup></sc>≤<italic>N</italic>(HI)<sc><10<sup>20.3</sup> cm<sup>−2</sup>.</sc> The objective of this study is to surpass the constraints of current research by utilizing deep learning methods to investigate a wider and more inclusive sample. This approach facilitates the detection and characterization of LLS with reduced column densities. We utilized high-quality spectral data simulated by the Dark Energy Spectroscopic Instrument (DESI) as the experimental foundation. Through the optimization of convolutional neural network (CNN) models, we have effectively boosted the model’s identification accuracy of LLS (with column densities of <sc>10<sup>18.5</sup> cm<sup>−2</sup></sc>≤<italic>N</italic>(HI)<sc>≤10<sup>20.0</sup> cm<sup>−2</sup>)</sc> in DESI simulated spectra to 95%. Following that, this paper validated the completeness and purity of the model under different signal-to-noise ratios and column density conditions. Additionally, an analysis of the differences between the CNN model’s estimated and actual values of column density and redshift was conducted. The analysis results indicate that, under conditions where the signal-to-noise ratio exceeds 6, for LLS with column densities of <sc>10<sup>19.0</sup> cm<sup>−2</sup></sc>><italic>N</italic>(HI)<sc>>10<sup>18.5</sup> cm<sup>−2</sup>,</sc> the completeness of the CNN model exceeds 0.5, and the purity exceeds 0.2. For LLS with column densities of <sc>10<sup>20.0</sup> cm