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  • Participatory Science and Machine Learning Applied to Millions of Sources in the Hobby–Eberly Telescope Dark Energy Experiment
Title: Participatory Science and Machine Learning Applied to Millions of Sources in the Hobby–Eberly Telescope Dark Energy Experiment
Abstract We are merging a large participatory science effort with machine learning to enhance the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX). Our overall goal is to remove false positives, allowing us to use lower signal-to-noise data and sources with low goodness-of-fit. With six million classifications through Dark Energy Explorers, we can confidently determine if a source is not real at over 94% confidence level when classified by at least 10 individuals; this confidence level increases for higher signal-to-noise sources. To date, we have only been able to apply this direct analysis to 190,000 sources. The full sample of HETDEX will contain around 2–3 million sources, including nearby galaxies ([Oii] emitters), distant galaxies (Lyαemitters or LAEs), false positives, and contamination from instrument issues. We can accommodate this tenfold increase by using machine learning with visually vetted samples from Dark Energy Explorers. We have already increased by over tenfold the number of sources that have been visually vetted from our previous pilot study where we only had 14,000 visually vetted LAE candidates. This paper expands on the previous work by increasing the visually vetted sample from 14,000 to 190,000. In addition, using our currently visually vetted sample, we generate a real or false positive classification for the full candidate sample of 1.2 million LAEs. We currently have approximately 17,000 volunteers from 159 countries around the world. Thus, we are applying participatory or citizen scientist analysis to our full HETDEX data set, creating a free educational opportunity that requires no prior technical knowledge.  more » « less
Award ID(s):
2008793
PAR ID:
10553184
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
DOI PREFIX: 10.3847
Date Published:
Journal Name:
The Astrophysical Journal
Volume:
975
Issue:
2
ISSN:
0004-637X
Format(s):
Medium: X Size: Article No. 172
Size(s):
Article No. 172
Sponsoring Org:
National Science Foundation
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    Abstract 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,DA(z), atz= 2.4, each to percent-level accuracy. This accuracy is determined primarily from the total number of detected Lyαemitters (LAEs), the false positive rate due to noise, and the contamination due to [Oii] emitting galaxies. This paper presents the citizen science project, Dark Energy Explorers (https://www.zooniverse.org/projects/erinmc/dark-energy-explorers), with the goal of increasing the number of LAEs and decreasing the number of false positives due to noise and the [Oii] 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 theDA(z) andH(z) parameters atz= 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. 
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