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

Aug 5, 2026

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Analysis leverages a citizen science campaign to enhance cosmological measurements from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). The experiment targets percent-level determinations of the Hubble expansion rate H(z) and angular diameter distance DA(z) at z=2.4, constrained by counts of Lyman-α emitters, noise-driven false positives, and contamination from [O II] galaxies. The Dark Energy Explorers project improves LAE selection, reduces spurious detections, and supplies machine-learning labels through human-visual classification, yielding millions of classifications from 11,000 volunteers across 85+ countries and expected 10–30% parameter accuracy gains.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Abstract

Introduction

Scientific motivation: cosmic acceleration

Measurement targets: H(z) and DA(z)

Current uncertainties and survey landscape

Role of HETDEX and Lyman-α emitters

Dark Energy Explorers citizen-science approach

arXiv :2304 .07348v1 [ astro-ph .IM] 14 Apr 2023 Draft version April 18, 2023 Typeset using LATEX twocolumn style in AASTeX63 Using Dark Energy Explorers and Machine Learning to Enhance the Hobby-Eberly Telescope Dark Energy Experiment Lindsay R. House ,1, 􀀃 Karl Gebhardt ,1 Keely Finkelstein ,1 Erin Mentuch Cooper ,1, 2 Dustin Davis ,1 Robin Ciardullo ,3, 4 Daniel J Farrow ,5 Steven L. Finkelstein ,1 Caryl Gronwall ,3, 4 Donghui Jeong ,3, 4 L. Clifton Johnson ,6, 7 Chenxu Liu ,8, 1 Benjamin P. Thomas ,1 and Gregory Zeimann9 1 Department of Astronomy, The University of Texas at Austin, 2515 Speedway Boulevard, Austin, TX 78712, USA 2 McDonald Observatory, The University of Texas at Austin, 2515 Speedway Boulevard, Austin, TX 78712, USA 3 Department of Astronomy & Astrophysics, The Pennsylvania State University, University Park, PA 16802, USA 4 Institute for Gravitation and the Cosmos, The Pennsylvania State University, University Park, PA 16802, USA 5 Centre of Excellence for Data Science, Arti􀀌cial Intelligence and Modelling (DAIM), University of Hull, Cottingham Road, Kingston-upon-Hull HU6 7RX, UK 6 Adler Planetarium, 1300 S. DuSable Lake Shore Dr. , Chicago, IL 60605, USA 7 Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) and Department of Physics and Astronomy, Northwestern University, 1800 Sherman Ave., Evanston, IL 60201, USA 8 South-Western Institute for Astronomy Research, Yunnan University, Kunming, Yunnan, 650500, People's Republic of China 9 Hobby Eberly Telescope, The University of Texas at Austin, Austin, TX, 78712, USA 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), at z = 2:4, each to percent-level accuracy. This accuracy is determined primarily from the total number of detected Lyman-􀀋 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 e􀀎cient and e􀀋ective tool for classi􀀌cation 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 identi􀀌cation above a signal-to-noise cut, and 3) providing labels for machine learning e􀀋orts. Since the end of 2022, Dark Energy Explorers has collected over three and a half million classi􀀌cations by 11,000 volunteers in over 85 di􀀋erent countries around the world. By incorporating the results of the Dark Energy Explorers we expect to improve the accuracy on the DA (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. Keywords: Dark Energy, Cosmology, Citizen Science 1. INTRODUCTION Supernovae observations discovered that the universe is undergoing an accelerated expansion (Riess et al. 1998; Perlmutter 1999; Riess et al. 2021), which has Corresponding author: Lindsay R. House [lindsay.r.house@gmail.com](lindsay.r.house@gmail.com) 􀀃 NSF Graduate Research Fellow been con􀀌rmed by a myriad of follow-up cosmological observations (Colless et al. 2003; Dawson et al. 2013; Tegmark et al. 2004; Planck Collaboration et al. 2020; DESCollaboration et al. 2021) . The community is struggling for a theoretical understanding of this acceleration (Albrecht et al. 2009), with the cosmological con

FAQs

What measurements does HETDEX aim to improve?

HETDEX focuses on percent-level measurements of the Hubble expansion rate H(z) and angular diameter distance DA(z) at z around 2.4.

How does Dark Energy Explorers support HETDEX data analysis?

It increases the number of reliable LAEs, lowers noise-related false positives, and reduces contamination from [O II] galaxies while providing labels for machine learning.

Why is human visual classification important in this pipeline?

The document states that classification is most accurately done by the human eye and is especially effective when combined with unsupervised machine learning.

Aug 5, 2026

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