Abstract
I will present on the citizen science project, Dark Energy Explorers, which we are using to improve the cosmological measures from HETDEX. These cosmological parameters are H(z) and D_A(z) at z = 2.4. Dark Energy Explorers has a goal of increasing the number of LAEs, decreasing the number of false positives due to noise and the [O II] galaxy contamination. We then take the results from Dark Energy Explorers and use it as labels for machine learning, specifically the t-SNE algorithm. By incorporating the results of the Dark Energy Explorers and machine learning we expect to improve the accuracy on the D_A(z) and H(z) parameters at z = 2.4” by 10 − 30%. Since the end of 2022, Dark Energy Explorers has collected over 4 million classifications by 11,000 volunteers in over 85 different countries around the world. I will briefly discuss how we have also used as way to educate the public on astronomy, HETDEX, and dark energy and strategies for outreach and engaging the public. While the primary goal is to improve on HETDEX, Dark Energy Explorers has already proven to be a uniquely powerful tool for science advancement and increasing accessibility to science worldwide.
