Searching for rare Ca II quasar absorption lines using deep learning

Quasar absorption lines (QALs), created by the light of celestial objects billions of light-years away, can be used to trace gas components from distant galaxies and thus are crucial to the study of galaxy evolution. Ca II QALs, in particular, are important for studying both star formation and recent galaxies because they are one of the dustiest QALs and are located at lower redshifts. However, Ca II QALs are quite difficult to detect, so the number of known Ca II QALs is extremely low, leaving many important models and theories unconfirmed. In this work, we developed an accurate and efficient approach to search for Ca II QALs using deep learning. We created large amount of simulation data for our training set, while we used an existing Ca II QAL catalog for our test set. We also designed a novel preprocessing method aimed at discovering weak Ca II absorption lines. Our solution achieved an accuracy of 96% on the test dataset and runs thousands of times faster than traditional methods. Our trained neural network model was applied to quasar spectra from the Sloan Digital Sky Survey's Data Releases 7, 12, and 14, and discovered 542 brand-new Ca II QALs and. This is currently the largest catalog of Ca II QALs ever discovered, which will play a significant role in creating new theories and confirming existing theories. Furthermore, our approach can be applied to the search of virtually any other type of QAL, opening up opportunities for ground-breaking research about galaxy evolution.