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Using Dark Energy Explorers and Machine Learning to Enhance the Hobby-Eberly Telescope Dark Energy Experiment

House, Lindsay R.; Gebhardt, Karl; Finkelstein, Keely; Cooper, Erin Mentuch; Davis, Dustin; Ciardullo, Robin; Farrow, Daniel J.; Finkelstein, Steven L.; Gronwall, Caryl; Jeong, Donghui; Johnson, L. Clifton; Liu, Chenxu; Thomas, Benjamin P.; Zeimann, Gregory

Authors

Lindsay R. House

Karl Gebhardt

Keely Finkelstein

Erin Mentuch Cooper

Dustin Davis

Robin Ciardullo

Steven L. Finkelstein

Caryl Gronwall

Donghui Jeong

L. Clifton Johnson

Chenxu Liu

Benjamin P. Thomas

Gregory Zeimann



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, D A(z), at z = 2.4, each to percent-level accuracy. This accuracy is determined primarily from the total number of detected Lyα 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 (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 [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.

Citation

House, L. R., Gebhardt, K., Finkelstein, K., Cooper, E. M., Davis, D., Ciardullo, R., …Zeimann, G. (2023). Using Dark Energy Explorers and Machine Learning to Enhance the Hobby-Eberly Telescope Dark Energy Experiment. The Astrophysical journal, 950(2), Article 82. https://doi.org/10.3847/1538-4357/accdd0

Journal Article Type Article
Acceptance Date Apr 13, 2023
Online Publication Date Jun 13, 2023
Publication Date Jun 20, 2023
Deposit Date Feb 29, 2024
Publicly Available Date Mar 5, 2024
Journal Astrophysical Journal
Print ISSN 0004-637X
Electronic ISSN 1538-4357
Publisher American Astronomical Society
Peer Reviewed Peer Reviewed
Volume 950
Issue 2
Article Number 82
DOI https://doi.org/10.3847/1538-4357/accdd0
Keywords Cosmology; Cosmological parameters; Astronomy education; Dark energy
Public URL https://hull-repository.worktribe.com/output/4394453

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0

Copyright Statement
© 2023. The Author(s). Published by the American Astronomical Society.
Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.




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