The Milky Way Project: Leveraging Citizen Science and Machine Learning to Detect Interstellar Bubbles

TL;DR

The paper introduces Brut, a machine learning algorithm based on Random Forests, that effectively identifies interstellar bubbles in infrared images by leveraging citizen science data, improving detection accuracy and discovering previously missed bubbles.

Contribution

Brut combines citizen science and machine learning to accurately identify interstellar bubbles, demonstrating improved detection and re-evaluation of existing catalogs.

Findings

Brut's bubble identification is comparable to expert astronomers.

10-30% of catalog objects are non-bubble interlopers.

Brut discovers bubbles missed by previous searches, especially near bright sources.

Abstract

We present Brut, an algorithm to identify bubbles in infrared images of the Galactic midplane. Brut is based on the Random Forest algorithm, and uses bubbles identified by >35,000 citizen scientists from the Milky Way Project to discover the identifying characteristics of bubbles in images from the Spitzer Space Telescope. We demonstrate that Brut's ability to identify bubbles is comparable to expert astronomers. We use Brut to re-assess the bubbles in the Milky Way Project catalog, and find that 10-30% of the objects in this catalog are non-bubble interlopers. Relative to these interlopers, high-reliability bubbles are more confined to the mid plane, and display a stronger excess of Young Stellar Objects along and within bubble rims. Furthermore, Brut is able to discover bubbles missed by previous searches -- particularly bubbles near bright sources which have low contrast relative to their surroundings. Brut demonstrates the synergies that exist between citizen scientists, professional scientists, and machine learning techniques. In cases where "untrained" citizens can identify patterns that machines cannot detect without training, machine learning algorithms like Brut can use the output of citizen science projects as input training sets, offering tremendous opportunities to speed the pace of scientific discovery. A hybrid model of machine learning combined with crowdsourced training data from citizen scientists can not only classify large quantities of data, but also address the weakness of each approach if deployed alone.