DeepSky: Identifying Absorption Bumps via Deep Learning

The pervasive interstellar grains provide significant insights to help us understand the formation and evolution of stars, planetary systems, and galaxies, and could potentially lead us to the secret of the origin of life. One of the most effective ways to analyze the dusts is via their interaction and interference on some background light. The observable extinction curves and spectral features carry the information about the size and composition of the dusts. Among the features, the broad 2175 Å absorption bump is one of the most significant spectroscopic interstellar extinction features. Traditionally, astronomers apply conventional statistical and signal processing techniques to detect the existence of absorption bumps. These approaches require labor-intensive preprocessing and the co-existence of some other reference features to alleviate the influence from the noises. Conventional approaches not only involve substantial labor cost in complicated workflows, but also demand well-trained expertise to make subtle and error-prone conditional decisions. In this paper, we propose to leverage deep learning to automate the detection workflow without minute feature engineering. We design and analyze deep convolutional neural networks for detecting absorption bumps. We further propose the framework of deep learning mechanisms and models (collectively called DeepSky) for scientific discovery in astronomy. The prototype of DeepSky demonstrates efficient and effective results using limited labeled data. With well-designed data augmentation, our trained model achieved about 99% accuracy in prediction using the real-world data.