  We present analysis using a citizen science campaign to improve the
cosmological measures from the Hobby-Eberly Telescope Dark Energy Experiment
(HETDEX). The goal of HETDEX is to measure the Hubble expansion rate, $H(z)$,
and angular diameter distance, $D_A(z)$, at $z =$ 2.4, each to percent-level
accuracy. This accuracy is determined primarily from the total number of
detected Lyman-$\alpha$ emitters (LAEs), the false positive rate due to noise,
and the contamination due to [O II] emitting galaxies. This paper presents the
citizen science project, Dark Energy Explorers, with the goal of increasing the
number of LAEs, decreasing the number of false positives due to noise and the
[O II] galaxies. Initial analysis shows that citizen science is an efficient
and effective tool for classification most accurately done by the human eye,
especially in combination with unsupervised machine learning. Three aspects
from the citizen science campaign that have the most impact are 1) identifying
individual problems with detections, 2) providing a clean sample with 100%
visual identification above a signal-to-noise cut, and 3) providing labels for
machine learning efforts. Since the end of 2022, Dark Energy Explorers has
collected over three and a half million classifications by 11,000 volunteers in
over 85 different countries around the world. By incorporating the results of
the Dark Energy Explorers we expect to improve the accuracy on the $D_A(z)$ and
$H(z)$ parameters at $z =$ 2.4 by 10 - 30%. 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.
