  The pervasive interstellar dust grains provide significant insights to
understand the formation and evolution of the stars, planetary systems, and the
galaxies, and may harbor the building blocks of life. One of the most effective
way to analyze the dust is via their interaction with the light from background
sources. The observed extinction curves and spectral features carry the size
and composition information of dust. The broad absorption bump at 2175 Angstrom
is the most prominent feature in the extinction curves. Traditionally,
statistical methods are applied to detect the existence of the absorption bump.
These methods require heavy preprocessing and the co-existence of other
reference features to alleviate the influence from the noises. In this paper,
we apply Deep Learning techniques to detect the broad absorption bump. We
demonstrate the key steps for training the selected models and their results.
The success of Deep Learning based method inspires us to generalize a common
methodology for broader science discovery problems. We present our on-going
work to build the DeepDis system for such kind of applications.
