Identifying the Absorption Bump with Deep Learning

TL;DR

This paper introduces a deep learning approach to detect the 2175 Angstrom absorption bump in interstellar dust extinction curves, offering a more efficient alternative to traditional statistical methods.

Contribution

The paper presents a novel deep learning methodology for identifying the absorption bump, reducing preprocessing needs and improving detection accuracy compared to traditional techniques.

Findings

Deep learning models successfully detect the absorption bump.

The approach reduces preprocessing complexity.

Potential for broader scientific discovery applications.

Abstract

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.