Abstract
In the age of large data astronomy, the use of machine learning (ML) is imperative. One method to ensure accurate results with ML is to use visual vetting. Dark Energy Explorers is crowd-sourced research, hosted on Zooniverse. To date, we have reached more than 16,000 individuals, representing over 80 countries. The goal is to visually vet sources from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). HETDEX is working to understand dark energy by mapping millions of galaxies at a lookback time of ~11 billion years. As an unbiased, spectroscopic survey one of the largest issues of HETDEX to tackle is minimizing false positives and contamination. Dark Energy Explorers has been successful with training the public to differentiate galaxies from artifact contamination to an accuracy of 98% as compared to an expert (House et al. 2023). In turn, DEE has improved the science impacts of HETDEX in combination with ML algorithms. Dark Energy Explorers and ML efforts have been successful in eliminating false positives from the source catalog (House et al. 2023) and have been able to remove almost 8,000 false detections with these efforts. The Dark Energy Explorers team has further used the project for public engagement in astronomy and ML through hosting Zoom nights with participants, facilitating discussions of the research publications, leading telescope tours, and creating classroom/educator resources. This talk will further explain how Dark Energy Explorers and ML have been successful in improving the science goals of HETDEX and public engagement.
