Deep Learning for Identification and Characterization of Ca ii Absorption Lines: A Multitask Convolutional Neural Network Approach - Yang Liu; Jie Li; Linqing Gao; Haotong Zhang; Zhenghua Xu; Yu Wang; Wenbin Lin (The Astrophysical Journal Supplement Series)
Quasar absorption lines are a powerful tool for studying the Universe, enabling us to probe distant gas, dust, and galaxy formation and evolution. However, detecting these lines, particularly Ca ii absorption lines, is a time-consuming and laborious process. Existing deep learning methods are prone to false positives and still require extensive manual verification and parameter measurement. This work presents three multitask convolutional neural network models and identifies the ResNet-CBAM model, which incorporates residual learning and an attention mechanism as the most effective. The results show that the ResNet-CBAM model achieves an accuracy of 99.7% in detecting Ca ii absorbers and excels in predicting critical parameters such as equivalent width and full width at half-maximum, with average correlation coefficients of 0.98 and 0.85, respectively. Furthermore, its remarkable generalization ability significantly improves detection precision on unseen data, rising from 20.3% of the cutting-edge model to 92.6%. In addition, with our numerous optimizations, our method can directly search for nonnormalized data, still achieving an accuracy of 98.6%. This translates to a dramatic reduction in manual inspection workload, paving the way for efficient and automated Ca ii absorber identification. In real-world applications on the Sloan Digital Sky Survey DR7 and DR12, our model successfully rediscovered 321 known Ca ii absorbers while identifying potential candidates in an additional 381 spectra. The codes used in this paper are available on Zenodo at doi: http://dx.doi.org/10.5281/zenodo.13953656 .